PG Program in Data Science, Machine Learning & Neural Networks
in collaboration with

Data Science is an integrative discipline of science that uses scientific procedures, processes, algorithms, tools, techniques, & technologies to take out knowledge and information from huge amounts of unstructured and untuned data.Data science is correlated with machine learning, data mining, and big data fields.

It amalgamates the concepts of data analysis, statistics, informatics, and their methodologies to learn and understand real events with data. It uses theories and methods of other disciplines such as statistics, mathematics, computer science, information technology, and many more.

Jim Gray, the Turing Award winner, considered data science as the “Fourth Paradigm” of science. With the rapid development of information technology and data explosion, science is changing expeditiously. According to a report by IT chronicles, organizations are generating around 2,000,000,000,000,000,000 bytes of data per day.

In this field, first of all, data is prepared for analysis, then data-driven solutions are developed and finally, the findings are presented to make high-level decisions from a wide spectrum of application domains. It comprises expertise from various branches such as:-

• Computer Science
• Statistics
• Information Science
• Mathematics
• Information Visualization
• Data Sonification
• Data Integration
• Graphic Design
• Complex Systems
• Communications and Businesses

Nate Silver and many other statisticians have asserted that data science is not a new field. They claim that it is another name for statistics while many others are in favor that it is different from statistics. Their claim is based on the fact that data science focuses on techniques and issues that are unique to digital data. Data science generally deals with quantitative and qualitative information like images in contrast to statistics that emphasizes on description and quantitative data.

There have been many instances in the past for early uses of data science. John Tukey, an American mathematician, and statistician who is best known for the Fast Fourier Transform algorithm (FFT) described data analysis which was similar to modern data science.

There are various techniques and technologies that data science utilizes which depend on several applications. Let us have a look at the following:

Data Science has become a popular technology in various sectors around the world owing to the massive explosion in data. It has become an important part for organizations for business intelligence and to make informed decisions.

Data Science course, certification, PGP’s are an entry level point to data science domain, aspirants from both the technical and non-technical domain are searching for an opportunity to get into data science and this data science course is one stop-solution for all your data science course requirements. Data science courses are a combination of

• Mathematics
• business acumen tools
• Algorithms
• Machine learning

That approach aids in the discovery of hidden insights or patterns in raw data that can be used in the formulation of key business decisions. In data science, both structured and unstructured data are dealt with. Predictive analytics is also used in the algorithms. As a result, data science is concerned with the present and future. That is, identifying trends based on previous data that can be valuable for current decisions, as well as identifying patterns that can be modelled and used to anticipate how things will look in the future. Statistics, tools, and business knowledge are all combined in Data Science. As a result, a Data Scientist's knowledge and comprehension of these topics is critical.

This Data Science Post Graduate Program, developed in collaboration with , will help you advance your career in Data Science by providing you with world-class training and expertise. The Data Scientist course provides in-depth instruction in the most in-demand Data Science and Machine Learning abilities, along with practical experience with major tools and technologies such as Python, R, Tableau, and Machine Learning ideas. Take your Data Science career to the next level by going deep into the complexities of data interpretation, understanding technologies like Machine Learning, and mastering advanced programming skills. DataScience course by DataTrained will help you to learn everything from scratch to advanced level. You should enroll in this course. It's totally worth it.

This collaboration between DataTrained and teaches students how to use an integrated Blended Learning method to become data science specialists. This Data Science course, developed in collaboration with , will prepare students for prominent data scientist jobs in the industry. The Data Science certification course is best suited for aspiring professionals with any educational background who have an analytical mindset, such as:

• Professionals in Information Technology
• Professionals in Information Technology Managers
• Business Analysts
• Professionals in Banking and Finance
• Marketing Managers
• Supply Chain Network Managers
• Freshmen or recent bachelor's or master's degree graduates

Data Science Courses are those that are aimed to create and enhance essential employability skills while also exploring issues of larger social or personal significance. In a nutshell, data science courses are aimed to enhance the knowledge of data science. However, data science is an umbrella term and there are numerous things to learn or to get specialization in a particular field such as

• Big data
• Machine learning
• Deep learning
• Data mining
• Python, etc.

There are multiple types of data science courses where you can learn different data science skills and get specialization in that specific course. Let’s have a look at the different types of data science courses based on different factors

Types based on the mode of education

• Online data science courses
  Online data science courses are the courses offered digitally. Course provider delivers the content or teaches the data science over multiple platforms such as zoom, google meet, or own build website. Online education is boosted during the corona pandemic. It has so many advantages over the traditional way of learning.
• Offline data science course
  The traditional way of learning, offline data science courses are courses offered by institutes where you’ve to be physically present in the classroom. It has face-to-face interaction with the faculty, however, it’s time-consuming and not a very efficient way of learning. Offline data science courses aren't as efficient as online data science courses.
• Hybrid data science courses
  It’s an integration and mixture of both types of modes of education that are online and offline. It combines the best of in-school and remote learning. There are few hybrid courses on data science since data science can be learned online and it's a more effective and efficient way of teaching and learning.

Types based on Specialization

• Data Science with Python
  Data science with python is a specialization course with python programming language. Python is a very popular language in computer science since it’s very easy to learn and understand. It’s a great tool too for data science professionals as it has many libraries on machine learning, deep learning, Natural language processing, etc. In this course, you will learn data science along with python.
• Data Science with R
  R is an open-source programming language and uses R in data science specifically for statistics and data visualization. Statistics is a pillar of data science and R is a powerful tool for statistical analysis. In the data science with R course, you’ll learn R programming and data science. You’ll get to know how you can apply R in a dataset.
• Data Science with Machine Learning
  Machine learning is a subset of Artificial learning. Machine learning is the ability of the computer to explicitly program itself. Data science is a crucial part of machine learning.

Types based on duration

• Diploma/Certification in data science
  Its most popular data science course is the Diploma in Data Science Courses, which is available in PG Diploma levels and aims to teach the basics of data science courses in a short period of 6-12 months and prepares students to find jobs correctly after 10+2.
• Bachelor degree in data science
  Bachelors in data science are three to four years of undergrad data science studies in the domains of science and engineering. Machine learning and artificial intelligence are also available. Admission to BTech programs is determined only by the Engineering Entrance Exams, whereas admission to BCA data science programs is determined solely by the merit of the class 12th grades.
• Master degree (PG) in data science
  After completing a bachelor's degree in data science, a master's degree in data science focuses on specialties and can be pursued. MSc/MTech/MCA Data Science is a popular master's degree in the data science program.

There are several courses available for Data Science course aspirants in the market. These courses are available in online, offline, hybrid and distance learning modes. Owing to the pandemic it is highly advisable to refrain from physical classes as it increases the risk of infections.

Our Courses are in collaboration with IBM or International Business Machines which is a world renowned organization. Together we have combined our forces to bring the best in class faculty. Our tutors and experts will be along with you at every stage of course.

If the student ever has any doubts they can raise their queries on a live chat platform. Students also have the option to raise a ticket on our learning management system and get them resolved within a short period of time. Let’s look at our data science courses in detail:-

Data Science Micro Degree Courses

Data Science Certificate Courses

So you can see DataTrained has a lot of data science courses to offer. So wait no more and join today to skyrocket your career.

Data science is an interdisciplinary field of study that employs scientific procedures, methodologies, methods, systems, and algorithms to extract required insights and information from structured and unstructured data. Big Data, Machine Learning, and Data Science Modeling are the three core components of the Data Science syllabus. Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Machine Learning, and Algorithms are among the primary topics covered in the Data Science course.

You should opt for this course to learn everything from scratch to the advanced level of data science syllabus, course subjects and all.

Big Data, Machine Learning, and Data Science Modeling are the three core components of the Data Science course syllabus. The subjects in these three main components cover a wide range of topics in this highly sought-after discipline. The following is the entire Data Science Syllabus:

• Data Science: An Introduction
• Statistical and Mathematical Skills
• Machine Learning
• Coding
• Algorithms used in Machine Learning
• Data Science Statistical Foundations
• Algorithms & Data Structures
• Techniques for Scientific Computing Optimization
• Visualization of Data
• Computations on Matrixes
• Models for Students
• Toolkits for Experimentation, Evaluation, and Project Deployment
• Clustering for Predictive Analytics & Segmentation Applied Mathematics and Informatics
• Analyze exploratory data
• Artificial Intelligence & Business Acumen

The syllabus is constituted into modules and these modules are further categorized into courses that have the topics. A good syllabus is structured that makes learning effective. Let's have a look at it

Module 1 Foundations

The foundation module provides all the basic details to get you started with the course. It creates a basic pillar for the course. This module contains 2 courses:

• Python for Artificial Intelligence & Machine Learning
• Applied Statistic

Module 2 Machine Learning

Machine learning is the science that studies how computers can learn without it being explicitly programmed. You’ll be learning how multiple models can be integrated to get a better predictive model in machine learning. Along with it, you’ll learn more algorithms used in machine learning such as decision trees, random forests, and bagging & boosting. You’ll be given machine learning projects too.

• Supervised Learning
• Ensemble Techniques
• Unsupervised Machine Learning

Module 3 Advanced Techniques

You’ll be learning all essential tools and techniques to do exploratory data analysis (EDA), data standardization, data visualization, and feature engineering. One of the most important parts of this course is building a model pipeline.

Module 4 Time Series Analysis

You'll learn the analysis of time series. Time series is the data taken against time intervals such as price over time. You'll get to know the basic but important fundamentals of time series analysis such as

• Time series components
• Stationarity
• Time series model
• Model evaluation

You'll be given assignments and projects for the practice as well so you could gain practical knowledge as well.

Module 5 Recommendation Engine

A recommender Engine is a data filtering system that uses machine learning algorithms to estimate a customer's ratings or preferences for a specific item. In this course, you’ll learn why recommendation engines are used and the applications such as how Netflix uses recommendation engines.

Module 6 Introduction to Deep Learning

In this course, deep learning, the different components of a neural network are examined in this introductory module, which begins with the adoption of Neural Networking terms. Install and familiarise yourself with the TensorFlow library, then make use of Keras' simplicity to build a powerful neural network model for a classification task. You'll also learn how to tweak a Deep Neural Network.

Module 7 Introduction to NLP

You'll discover how to teach a computer to acquire languages and then expect it to completely comprehend them using proper algorithms. This system will walk you through an overview of NLP as well as all of its major components. Natural Language Processing (NLP) is a branch of computational linguistics that is used to create real-world applications that deal with a variety of languages.

Module 8 Tableau

You'll learn how to use Tableau to visualize data and generate possibilities for you or key decision-makers to find data patterns like consumer buy behavior, sales trends, or manufacturing bottlenecks.

You'll learn how to use Tableau's tools to simply, rapidly, and aesthetically examine, experiment with, fix, prepare, and display data. You'll also learn how to

• Connect with any data set
• Analyze and interpret data with integration of calculation
• Visualization in the form of a map, graph, pie chart, etc.

Module 9 Power BI

Power BI is swiftly establishing itself as the world's most powerful self-service business intelligence platform, and an indispensable tool for both data pros and novices. This program will prepare you why Power BI provides a comprehensive collection of Business Intelligence tools for your data management goals, and how to use these tools to complete tasks. Imagine being able to swiftly organize your data, do simple calculations on it, and generate and publish attractive charts in only a few minutes.

We live in a time where everything revolves around data. As per a report by Tech Jury, In 2020 each human created about 1.7 Mb of data per second. The whole digital world is expected to have around 44 trillion gigabytes of data right now. Organizations have realized the benefits of data mining and use data science tools to their advantage and business intelligence. Let us have a look at the various data science tools:-

1. Data science tools to manipulate big data

• MS Excel: Excel is a spreadsheet and a part of the MS Office suite. It was developed by Microsoft Corporation. It is used to organize data and perform business functions like financial analysis. It is very easy to understand even by non-technical individuals and depicts data in rows and columns. It offers various formulas, mathematical, statistical, and logical operations.
• Apache Hadoop: It is a group of open-source software for solving problems involving huge amounts of data and computations. Its storage part is known as Hadoop Distributed File Systems or HDFS in short. It also has a processing part that is the Map Reduce Programming model.
• SQL: It is a domain specific language which is used for the management of data that is held in relational database management system or RDBMS. SQL is specially used for handling structured data. It is bifurcated into the following language elements:
  1. Clauses
  2. Expressions
  3. Predicates
  4. Queries
  5. Statements
  6. Insignificant white space
• Apache Spark: It is an open source analytics engine that is used for large scale data processing. Its key features include:
  1. Processing of data in real time streaming and in batches.
  2. Fast execution of distributed ANSI SQL queries for ad hoc reporting and dashboard.
  3. Performing EDA on large scale data without down sampling.
  4. Training machine learning algorithms.
• MySQL: This is a free and open source RDBMS, created by Swedish company MySQL AB. It is used by database apps like Joomla, WordPress, phpBB, etc, and many websites like YouTube, Twitter, Facebook, etc. Some of its features includes:
  1. A broad subset of ANSI SQL 99
  2. Cross-platform support
  3. Stored procedures
  4. Triggers
  5. Cursors
  6. Update views
• Neo4J: It is a graph database management system and was developed by Neo4j. It offers quick read and write performance while securing data integrity.

2. Data science tools for machine learning

• Python: It is a high level language which is used for general purposes. It is dynamically typed and garbage collected. It is based on code readability with significant indentation.
• SAS: SAS stands for Statistical Analysis System. This software is developed for data analysis and report writing.
• R: This programming language is used for tasks involving graphics and statistical computing.
• MATLAB: It is an abbreviation for MATrix LABoratory. It is used for:-
  1. Matrix manipulations
  2. Plotting of data and functions
  3. Implementing algorithms
  4. Creating user interfaces
  5. Interfacing with programs written in other languages
• DataRobot: It is an AI cloud platform used for building and deploying predictive models.
• BigML: It is a comprehensive machine learning platform for solving real world problems by using a single standardized framework.

Data science tools for data mining and transformation

• Pandas: It is a software library created for Python language and is used for data manipulation and analysis.
• Scrapy: It is an open-source and free web crawling framework which is also used for data extraction. It is written in a python programming language.
• Weka: It is an assembly of ML algorithms for data mining. It contains tools for:-
  1. Data preparation
  2. Classification
  3. Regression
  4. Clustering
  5. Association rules mining
  6. visualization

4. Data science tools for model deployment

• TensorFlow.js: It is used for developing machine learning models in JavaScript.
• MLflow: It is open-sourced and is used in the management of end to end ml lifecycle.

5. Data science tools for data visualization

• Tableau: Tableau is a visual analytics platform used for easy understanding of data. Its feature include:-
  1. Business intelligence
  2. Data Visualization
  3. Data Collaboration
  4. Data Blending
  5. Real-time data analysis
• ggplot2: It is a data visualization tool used for creating graphics or complex plots by the use of data.
• D3.js: It is a JavaScript library for handling docs based on data.
• Orange: This tool offers open-source machine learning and data visualization.

So you can see that there are so many tools that are offered for data science and we at DataTrained train you in each of them in our various Data Science courses which are available at an affordable price.

The domain of data science is broad and has its own set of benefits and drawbacks. So, we'll weigh the benefits and drawbacks of Data Science here. We will assist you in evaluating yourself and selecting the most appropriate Data Science course.

Advantages:-

• It is in High Demand
• Ample Number of Positions
• A High-Paying Career
• Data science can be used in a variety of ways.
• Data Science Improves Data
• Data Scientists are in High Demand
• There Will Be No More Boring Tasks
• Data Science Improves Product Intelligence
• Data Science Has the Potential to Save Lives
• Data Science Can Help You Improve Your Personality

Disadvantages:-

• The Term "Data Science" is a Bit misleading
• It's nearly impossible to master data science.
• Requires a huge amount of domain knowledge
• Random Data Can Lead to Remarkable Results
• The Issue of Data Privacy

While Data Science has many economic benefits, it also has its disadvantages.

When it comes to choosing the right Data Science course. I always recommend DataTrained’s PG Program in Data Science, Machine Learning & Neural Networks which is in collaboration with . This is one of the best courses available in the market. This Data Science PG Program equips you with the necessary information, skills, technology, and expertise to pursue a rewarding career in an area with many employment opportunities. This course will help you learn everything from scratch to expert level. This Data Science PG Program is a way to see your dreams come true. New batches for this program are soon, enroll now to avail this golden opportunity.

We've heard a lot about the potential of data science and how it can help businesses achieve tremendous results in the last decade. Of course, this outcry has resulted in astronomically expensive pay and incomprehensible benefits for certified Data Science specialists across India. Especially in Bangalore, which is ranked first and provides several chances for budding data scientists.

With so many training institutes providing Data Science programs and courses throughout the Indian subcontinent's geographical palette, it's no surprise that many professionals are swimming into the Data Science ocean to earn the title of Data Scientist.

While deciding to invest in a program we need to consider different constraints such as finance, time, location, institute, etc. The first and foremost is finance and plays a crucial part when it comes to deciding the program or institute. The fee depends on a lot of factors. Therefore, let's talk about it while keeping in mind the factors:

Data Science Average fee-based on degree: Data Science Average fee-based on location:

Data Science course fee changes according to the institute, the market value of that institute, location, and duration of the course. There are so many factors to consider and play an important role in determining the price of a course. It depends on the candidate what he/she desires.

As we have seen from the above, if you’re looking for an affordable place to learn data science then Delhi, India region is perfect for you. If you just want to upskill your data science skills then, certification or Bootcamp would be the best for you.

Yes, there are many platforms that give the basic outline of the program for free. Students can also find free videos for various data science topics on websites like YouTube. Some institutes also give students free access to demo course videos for a few days.

From free sources, you can only get a general idea of the data science domain. This knowledge is good only up to a point. It is very important that you get detailed knowledge of every topic and tool and also an industry valid certification too.

Students need to understand that there are some institutes that are even offering Data Science courses at INR 6 Lakh too! This is a relatively very high price. They have put such a huge number owing to the over hype created on the name Data Science courses.

DataTrained offers you data science courses at many affordable and economical prices. We upgrade your skills with world-class certifications in collaboration with . We cover all the topics and tools related to a particular data science course with in-depth guidance from industry experts. We also offer free internships.

Let us look at the salient features of our data science courses:-

• Flexible timings
• 100% online content
• On-demand interactive videos
• Live Internships included
• 360 Degree career support
• Unique specializations
• Ideal for both Working Professionals and Fresh Graduates
• Projects and Case Studies included
• One-on-One guidance from Industry Mentors
• Instant Doubt Resolution

Courses' length and duration also depend on the topic included in the program. For instance, our DataTrained program offers different programs according to customer needs.

The next question is ‘how would you decide that you want to pursue a degree or a certificate?’ You can ask yourself these questions to know whether you want to go for a certificate or a degree?

1. Figure out how fast you'll need to get your credentials.
2. Balancing a career or school
3. How far do you want to go in that career
4. Think about where you're at in your career right now.

A certificate may be useful if you are seeking entry-level employment. If you wish to be a data scientist, for example, you may get started right immediately after receiving a degree in data science. You can always go back to school to get a degree in data science. If you have job experience and want to work in management, a degree may be beneficial, as it is a prerequisite for many upper-level roles.

• Data science courses are taken by students with backgrounds in engineering, economics, statistics, mathematics, and computer science. Data science courses are also open to students with non-traditional backgrounds, such as finance or management.
• Class 12 (for bachelors in data science) with 50 percent aggregate marks and clarity of basic mathematics and statistics concepts are the basic data science course qualifying criteria (Probability, Calculus, Algebra).
• When applying for a master's degree in data science, candidates must have a minimum of 50% in their undergraduate degree(engineering/maths/science/commerce/economics/finance).
• Must have a basic understanding of programming languages such as Python, C, C++, Java, or R, which have major applications in data science.
• Graduates of data science should be able to write a basic SQL query and understand machine learning and its techniques in order to be ideal candidates for data science jobs.

Anyone interested in learning Data Science, whether a newbie or a seasoned practitioner, can enroll. Part-time or external Data Science programmes are available for engineers, marketing professionals, software developers, and IT professionals. Basic high school level studies are the minimal need for conventional Data Science courses.

Data Science is a loose synthesis of principles from mathematics, computer science, and statistics. Students should hold a bachelor's degree in one of the science, technology, engineering, or mathematics subjects (STEM background).

Students from other fields, such as business studies, are also eligible to take Data Science courses. Business professionals with a bachelor's or master's degree in business administration, such as a BBA or MBA, are also eligible to pursue advanced studies in the Data Science field.

In more depth, Data Science can be defined as a concept that combines statistics, data analysis, and techniques to analyse and make sense of real-world occurrences using data.

There are various courses available online and offline in the name of data science but most of them are of no value. Organizations look for professionals with certification from a reputed institute. DataTrained presents you with the Best Online Data Science courses available in India.

Our course is in collaboration with or International Business Machines. DataTrained is India’s number 1 Educational Tech startup and is a world-renowned organization. We have joined forces to make you an industry-ready certified data science specialist. We at DataTrained have already transformed 12,000+ careers. Our experts have designed this course in such a way that even a complete beginner can grasp every concept in detail from scratch.

We provide weekend live classes keeping in mind that working individuals would also be enrolling in our courses. In this way they don’t have to leave their current employment and freshers can also benefit from this arrangement. We train our students with multiple mock interviews with guidance from industry experts and professionals. We provide students with industry-based real data projects and exercises.

If the student ever has any doubts they can raise their queries on a live chat platform. Students also have the option to raise a ticket on our learning management system and get them resolved within a short period of time.

On the name placement, many institutes find job listings from job portals like Naukri, Indeed, etc, and inform their students about companies for interviews. After facing rejections in interviews many students get heartbroken and mentally tormented.

Here are the salient features of our data science course:-

• Sessions with industry professionals on a one-on-one basis.
• Suitable for both working professionals and college students and freshers.
• Hours of on-demand learning content.
• Instant doubt clearing.
• Case studies and projects included.
• Interactive and Immersive learning.
• Certificate of completion from DataTrained and .
• Student support and guidance.
• Live internships

A data scientist is a professional individual who is in charge of gathering, analyzing, and interpreting massive volumes of data. Mathematicians, scientists, statisticians, and computer professionals are examples of conventional technical positions that have evolved into data scientists. Advanced analytics technologies, such as machine learning and predictive modeling, are required for this position.

To understand the Data Scientist position, we’ve to look at the job responsibilities:

• To get insights, look for patterns and trends in data.
• To predict outcomes, develop algorithms and data models.
• Improve the quality of data or product offers using machine learning techniques.
• Other teams and senior employees will be informed of recommendations.
• In data analysis, use data technologies like Python, R, SAS, and SQL.
• Keep up with the latest developments in data science.

According to the Harvard Business Review, “Data Scientist is the sexiest job of the 21st century”. Data Science is growing rapidly and with that growth of data science, the requirement of data scientist growth because enormous volumes of data enable digging down to uncover tiny abnormalities in data that might disclose security system flaws, data science plays a critical role in security and fraud detection. There are so many applications of data science in numerous ways such as predicting consumer behavior, consumer segmentation, prediction of future prices, etc.

The Data science sector grew 650% since 2012 and the future is even better since data has surpassed oil in value. The US Bureau of Labor Statistics forecasted that demand for data science skills will increase by 27.9% by 2026

Salary of Data Scientist

According to Glassdoor, a Data Scientist's average salary for entry-level is ₹10,00,000 per annum, and this figure could go as high as ₹21,00,000 per annum in India. This figure is based on 5061 salaries reported to glassdoor anonymously.

Salary depends on so many factors such as experience, location, company. To get a clearer picture. Let’s look at it further.

Salaries based on Experience Salaries based on location Data Scientist and Data Analyst

Data Scientist & Data Analyst seems similar and occasionally used interchangeably as a synonym by beginners. They both have matching job responsibilities. However, Data scientists have more responsibilities and are also viewed as more senior than Data Analyst positions. Let’s have a look at the key differences between Data scientists and Data analysts.

Data science is a quite broad field; it encompasses a lot of other subjects and topics. It has become an essential part of day-to-day business operations and the most sought-after discipline of students in today’s times. It includes processes like machine learning, data visualization, cluster analysis, data mining, etc.

Organizations employ data scientists to carry out a variety of tasks including products, engineering, sales, and marketing. There are tons of jobs in the market requiring data science profiles. Different data science profiles require different educational requirements and skills.

However, the basic skills in every data science field are somewhat similar. Also based on the kind of work requirements, the salary package is also different from company to company. It also depends on the location and different sectors as well.

For example, according to a report by IDC studies, the US is the biggest market for data and analytics. Followed by Western Europe in the 2nd position and then followed by the United Kingdom and Japan.

Let's first look at the basic requirement for every type of data scientist roles:-

• Programming languages required:
  • SQL
  • Python
  • R
  • Java or JavaScript
  • C, C++, C#
• Tools knowledge requirements:
  • Machine learning platforms like:
    • Jupyter Notebooks
    • MATLAB
    • KNIME
    • MS Azure learning studio
    • IBM Watson Machine Learning
  • BI tools like:
    • Tableau
    • Power BI
    • Looker
    • QlikSense
  • Relational Databases like:
    • MS SQL Server
    • PostgreSQL
    • MySQL
    • Oracle
    • HIVE
    • Snowflake
  • Cloud platforms like:
    • Amazon Web Services
    • Microsoft Azure
    • Google Cloud Platform
• Technical skills requirements:
  • Programming skills
  • Data manipulation, analysis and visualizations
  • Data modeling
  • Model building, testing, and deploying
  • Machine learning
  • Artificial intelligence
  • Cloud computing
  • Application Programming Interfaces (APIs)
  • Statistics and Mathematics

Now let’s look at the types of data scientists based on different job titles or roles:-

• Data Analyst:
  
  

  A data analyst is someone who performs tasks related to data analysis and reporting. They are responsible for gathering, organizing, and cleaning data. They are required for the visualization of data and convey the results of the analyses.

  Along with the above-mentioned requirements, they should be experts in SQL and Python for statistical work and automation. They should also be knowledgeable in Jupyter notebook and SQL IDEs.

• Machine Learning Engineer/scientist:
  
  

  Machine Learning professionals are innovative in their approach and execute new algorithms. They develop algorithms that recommend price strategies, products and derive patterns from huge inputs of data and demand forecasting.

  Additionally, they are required to be skilled in programming languages such as Julia, Scala, and application frameworks like Django and Flask.

• Software Programming Analysts:
  
  

  These types of data scientists perform calculations using programming. They are basically required to do automation of routine for big data-related activities and minimize computational time.

  They should have technical skills like software architecture, development, and testing. Along with that database design, data warehousing, and database administration.

• Actuarial Scientist:
  
  

  Actuarial science is used by financial institutions and banks for predicting the market conditions and forecasting future incomes, revenue growth, and profit & losses by performing mathematical algorithms.

• Database Admin:
  
  

  These types of data scientists are responsible for the administration of databases. It includes ensuring the availability of the database, data security, and integrity along with superior database performance. They should have knowledge of pgAdmin 4, php MyAdmin, and SQL server management studio.

• Data modeler:
  
  

  They are required for designing, improving, and maintaining data models. That in turn, they translate to database implementation. It is performed to enhance data availability & performance of databases. They have to work along with data admins and data architects in coordination to perform these tasks.

• Data Engineer:
  
  

  Data engineers are responsible for designing, building, and managing the data stored by an organization. They are required to create a data handling infrastructure for analyzing and processing data according to the company’s requirements.

  They must be proficient in programming languages like Scala, Go and Extracting, Transforming and Loading (ETL) tools like Microsoft SQL Server Integration Services, XPlenty, Talend, and Cognos data manager.

• Data Architect:
  
  

  Data Architects are responsible for developing the whole architecture of data management keeping in mind the company's business needs. They are also required for designing a framework for data collection, usage, modeling, retrieval, and security.

  They must be proficient in pgAdmin 4, SQL server management studio, Apache Hadoop, Cassandra, MongoDB, DbSchema, Draw.io, etc

• Statistician:
  
  

  Statisticians are more oriented towards statistics and data analysis. They are responsible for analyzing data, applying statistical methods to data, and identifying trends and patterns for informed decision making and business intelligence.

  They have to be knowledgeable in statistical analysis tools like Statistical Package for the Social Sciences (SPSS), MATLAB, and Statistical Analysis System (SAS).

• Business Intelligence Developer:
  
  

  This type of data scientist is responsible for data visualization, dashboard creation, ad hoc reports. A business intelligence developer has to be proficient at dashboarding tools like Tableau.

• Marketing Scientist:
  
  

  Market scientists are responsible for customer value and handling the organization’s profit and growth. With the knowledge of data science, they analyze performance and boost efficiency.

• Business Analyst:
  
  

  Their purpose is to lower the costs and enhance the organization’s efficiency and business intelligence. They have to analyze organizational processes & systems and put forward solutions.

• Quality Analyst:
  
  

  They are generally employed in manufacturing industries. They are tasked with the preparation of interactive data visualizations for decision-making purposes.

• Spatial Data Scientists:
  
  

  Spatial Data scientists are required for making use of spatial data for navigation and site selection from a number of GPS apps like Google Maps, Apple maps, Bing maps, etc.

There are numerous paths to become a Data Scientist, but because it is a high-level employment, Data Scientists have usually been well-educated, holding degrees in mathematics, statistics, and computer science, among other fields. However, this is beginning to change.

In 8 easy steps, you can become a data scientist:

• Develop the necessary data skills.
• Learn the principles of data science.
• For data science, you'll need to learn some key programming languages.
• Develop your practical data abilities by working on data science projects.
• Create visualisations and practise giving them to others.
• Create a portfolio to demonstrate your data science abilities.
• Boost your internet presence
• Apply for Data Scientist jobs that are relevant to you.

There are various companies hiring data scientists in India and Abroad. Let’s have a look at companies hiring data scientists in India first:

• AB InBev India
  • Location: Bengaluru
  • Focus Areas: FMCG, drinks, financial services, IT
  • Sector: Consumer Goods
  • Average Salary: INR 16,08,000 per year
• AbsolutData
  • Location: Gurugram
  • Focus Areas: IoT, Big data, SaaS, Business Intelligence, Artificial Intelligence, Machine Learning
  • Sector: Information Technology and Services
  • Average Salary: INR 10,00,000 per year
• Accenture
  • Location: Bengaluru
  • Focus Area: Management consulting, business process outsourcing, blockchain, artificial intelligence.
  • Sector: Information technology and services
  • Average Salary: INR 10,93,000
• Adani Group
  • Location: Ahmedabad
  • Focus Area: Asphalt products manufacturing
  • Sector: manufacturing
  • Average Salary: Not disclosed
• Adobe
  • Location: Bengaluru, Noida
  • Focus Area: Data management, email marketing, advertising
  • Sector: Finance, telecommunication, etc
  • Average Salary: INR 28,03,000 per year
• Airbnb
  • Location: Bengaluru
  • Focus Area: Travel accommodations, hospitality
  • Sector: Internet
  • Average Salary: Not disclosed
• Alstom
  • Location: Bengaluru
  • Focus Area: Rail infrastructures, rail vehicles, signaling services, turnkey system
  • Sector: Transportation
  • Average Salary: INR 11,40,000 per year
• Amazon
  • Location: Various locations throughout India
  • Focus Area: Retail goods, consumer electronics, Amazon Web Services, amazon prime, amazon local
  • Sector: various sectors
  • Average Salary: INR 13,80,000 per year
• Antuit.ai
  • Location: Bengaluru
  • Focus Area: marketing, price analysis, forecasting, supply chain analytics
  • Sector: Computer science
  • Average Salary: INR 13,19,000 per year
• Axis Bank
  • Location: Mumbai
  • Focus Area: Banking services, financial services, cash management services
  • Sector: Banking
  • Average Salary: INR 13,05,000 per year
• Bloom AI
  • Location: Gurugram
  • Focus Area: Machine Learning, sales, and marketing
  • Sector: Information Technology and Services
  • Average Salary: Not disclosed
• Blueocean
  • Location: Pune
  • Focus Area: Information technology, consulting services, BPO, KPO
  • Sector: Management consulting
  • Average Salary: Not disclosed
• Boeing
  • Location: New Delhi
  • Focus Area: Manufacturing and selling airplanes, rotorcraft, rockets, satellites
  • Sector: Aviation and aerospace
  • Average Salary: INR 19,30,000 per year
• BYJU’S
  • Location: Bengaluru
  • Focus Area: Ed Tech
  • Sector: Education
  • Average Salary: INR 8,50,000 per year
• Capgemini
  • Location: Pune
  • Focus Area: Outsourcing, application management, business consulting
  • Sector: IT and Services
  • Average Salary: INR 9,60,000 per year
• Cisco
  • Location: Bengaluru
  • Focus Area: Networking, cloud, virtualization
  • Sector: Computer Networking
  • Average Salary: 15,60,000 per year
• Cognizant
  • Location: Hyderabad
  • Focus Area: Artificial Intelligence, cloud enablement, digital engineering
  • Sector: capital markets, information services, healthcare, life sciences, etc
  • Average Salary: INR 9,21,000 per year
• Citi
  • Location: Various locations throughout India
  • Focus Area: Investment banking, commercial banking, transaction services, etc
  • Sector: financial services
  • Average Salary: INR 16,50,000
• Colgate - Palmolive
  • Location: Mumbai
  • Focus Area: Consumer products manufacturing
  • Sector: financial services
  • Average Salary: Not disclosed
• Cyient
  • Location: Hyderabad
  • Focus Area: Engineering services, technology solutions, semiconductor
  • Sector: IT and services
  • Average Salary: INR 10,12,878 per year
• Dassault Systems
  • Location: Pune
  • Focus Area: Computer hardware and software
  • Sector: IT
  • Average Salary: Not disclosed
• Dell Technologies
  • Location: Various locations throughout India
  • Focus Area: Digital transformation
  • Sector: IT services
  • Average Salary: INR 14,71,565 per year
• Flipkart
  • Location: Bengaluru
  • Focus Area: E-commerce
  • Sector: retail shopping, e-commerce
  • Average Salary: INR 14,00,000
• Genpact
  • Location: New Delhi
  • Focus Area: Banking and financial services, capital market, telecom, logistics
  • Sector: IT & services
  • Average Salary: Not disclosed
• HCL Technologies
  • Location: Noida
  • Focus Area: Engineering and research & development, digital business solutions, hybrid cloud services
  • Sector: IT and communications
  • Average Salary: INR 11,71,098
• Honeywell
  • Location: Gurugram
  • Focus Area: Aerospace, automation, safety solutions
  • Sector: Electrical and electronics
  • Average Salary: INR 14,70,000
• HP
  • Location: Bengaluru
  • Focus Area: Personal computers, printers, business solutions
  • Sector: various sectors
  • Average Salary: INR 15,15,000
• IBM
  • Location: Bengaluru
  • Focus Area: BPS, Hybrid multi-cloud services, IoT services, BPO
  • Sector: Information technology and services, business development
  • Average Salary: INR 9,00,000
• Indeed
  • Location: Hyderabad
  • Focus Area: Internet services
  • Sector: Information technology
  • Average Salary: INR 4,50,000 per year
• Lenskart
  • Location: Faridabad
  • Focus Area: Eyewear, sunglasses, opticals, home eye tests, and trials
  • Sector: e-commerce, healthcare
  • Average Salary: Not disclosed
• LinkedIn
  • Location: Bengaluru
  • Focus Area: Career development
  • Sector: Various sectors
  • Average Salary: INR 24,91,000
• Microsoft
  • Location: Various locations throughout India
  • Focus Area: MS Office suite, windows, Xbox, etc
  • Sector: various sectors
  • Average Salary: INR 18,58,000
• Mindtree
  • Location: Bengaluru
  • Focus Area: Data and intelligence, cloud services, consulting services
  • Sector: healthcare, banking, capital markets, insurance, travel hospitality, etc
  • Average Salary: INR 12,81,000
• Reliance
  • Location: Mumbai
  • Focus Area: Retail business, telecom, consumer goods, energy
  • Sector: Retail, telecommunications
  • Average Salary: INR 18,02,850
• TCS
  • Location: Mumbai
  • Focus Area: consulting services, BPS, Information technology
  • Sector: Information technology
  • Average Salary: INR 6,19,082
• Tech Mahindra
  • Location: Multiple locations throughout India
  • Focus Area: Infra and cloud services, data analytics, digital supply chain, intelligent automation, etc
  • Sector: Communication, banking, financial services, hospitality, oil, and gas, etc
  • Average Salary: INR 7,30,000
• Wipro
  • Location: Various locations
  • Focus Area: Data analytics, consulting services, infra services
  • Sector: Multiple sectors
  • Average Salary: INR 9,26,000

The list of companies hiring data scientists abroad are:

• Pinterest
• Snap Inc
• Microsoft
• Accenture
• Oracle
• Slack
• Lyft
• Intel
• Uber
• Crayon Data

A data scientist's average annual pay is Rs.6,98,412. An entry-level data scientist can earn almost Rs.5,00,000 per year with less than a year of experience. Data scientists with 1 to 4 years of experience may expect to earn around Rs.6,10,811 per year on average.

Salaries Paid to Data Scientists by different companies in India.(Source)

Data scientists look into the future. They begin with big data, which is defined by the three Vs: volume, variety, and speed. The data is then used to feed algorithms and models. Working in machine learning and AI, the most cutting-edge data scientists create models that automatically self-improve, recognizing and learning from their failures.

According to a report, the worldwide data science industry is expected to reach USD 115 billion in 2023, with a CAGR of 29%. According to a Deloitte Access Economics survey, 76 percent of organizations aim to raise their data analytic spending over the next two years. Data science and analytics can aid almost any industry. However, some industries are better positioned to benefit from data science and analytics than others.

Usage of data science in Banking & Finance Industry

Finance was one of the first industries to use data science. Every year, businesses were fed up with bad loans and losses. They did, however, have a lot of data that was acquired during the first filing for loan approval. They decided to hire data scientists to help them avoid losing money.

• Fraud detection
• Credit risk analysis
• Risk modeling
• Lifetime value prediction

Usage of data science in Healthcare Industry

A Data Scientist's job is to use all of Data Science's approaches to integrate it into healthcare software. To create prediction models, the Data Scientist derives meaningful insights from the data.

• Virtual Assistance
• Drug Research
• Medical Image Analysis

Usage of data science in E-Commerce Industry

The industry of e-commerce can predict many things on the basis of their behavior, they can anticipate purchases, earnings, and losses, as well as urge clients to buy more things. Purchase data is also used by businesses to develop psychological pictures of customers in order to promote items to them and increase client loyalty, resulting in increased income. Let’s see how data can be applied in e-commerce.

• Customer Sentiment Analysis
• Recommendations Engines
• Prize Optimization
• LifeTime Value Prediction

Usage of data science in Education Industry

People's lives are shaped by their education. It has the ability to change and enrich people's lives. Humans have grown via education and created techniques to improve education from the birth of civilization. Now data science is integrating with the education sector.

Data Science in Education allows you to have centralized control over all student data, allowing you to evaluate the performance of students and take appropriate action. This study will help to make modifications that will aid the kids and will assist them in solving their difficulties in any manner feasible.

Usage of data science in Entertainment Industry

Forecasting, operations research, topic modeling, user segmentation, and content suggestions may all benefit from data science insights. Data is used by streaming providers like Amazon and Netflix to determine which shows are approved and promoted. At the Wharton Customer Analytics Initiative Conference in 2015, Dave Hastings, Netflix's head of product analytics, observed, "You don't make a $100 million investment these days without an awful lot of analytics." Meanwhile, data scientists at 20th Century Fox have employed AI to examine movie trailers in order to figure out what moviegoers could enjoy. The role of data science in entertainment has only risen in the years thereafter.

• Customer Sentiment Analysis
• Real-time analytics
• Recommendation engine

Usage of data science in Transportation & Logistic Industry

Machine Learning has the ability to revolutionize the Logistics and Transportation business by identifying the most critical aspects for a supply network's performance while also learning in the process.

• Reduction of freight cost through delivery path optimization
• Matching supply with demand
• Estimating delivery time
• Inventory management

Usage of data science in Telecommunications Industry

Telecom Industries can no longer use old approaches and procedures to handle the massive amounts of data that are being generated every minute. As a result, they are turning to modern Data Science tools to make use of this information.

For instance, if a telecommunications company wants to build a signal transmission tower at a specific place, however, whether it will be profitable to invest or not can be known by data analytics. Data science bring improvement in telecommunications by:

• Product Optimization
• Better network security
• Predictive analytics
• Real-time analytics

Click here to learn more about Data Science and its application in industries.