Post Graduation in Data Science

This is an induction module where you will start understanding the current market scenario and what it takes to make a mark in the data analytics domain.

You will learn how analytics enables companies across the globe to understand customer trends and patterns and how it impacts overall revenue. Research shows 95% of companies state managing unstructured data as one of the biggest challenges. There is no better time than now to become a data expert who understands how to handle data and derive insights from them.

This module will help you evaluate your skills and intent to become a successful data scientist. You will understand the importance of the core skills that make up a good data scientist. While subject matters are important, you will learn and evaluate a few more skills here:

• To simplify complicated ideas and explain to absolute novices.
• To be very curious about a business, understand market trends, and know how to hit the bull’s eye with your research and solutions.
• To be keen on working with AI and machine learning tools.
• To look at the bigger picture and understand the organization’s architecture rather than just focusing on individual tasks.

Simultaneously, you will learn how data science has the power to mitigate risks and, when done incorrectly, can incur heavy losses. You will learn to adopt data science tools for various domains/fields.

From different data science and analytics tools to what kind of organizations are best suited to apply – you will get a brief of the entire curriculum in this module. Treat it as a prep-up module.

You will go over the basics of statistics and understand how it helps determine patterns through numbers. This module is essential to help candidates who don’t have any prior knowledge of statistics.

Simultaneously, you will brush up on your knowledge of the basics of programming for non-programmers. This module aims to break the myth that computer science is all about programming. Infact, it includes graphics, operating systems, logical functions, theoretical computer science, computer architecture, and algorithm design.

Lastly, this module will help you recapitulate the basics of calculus, linear algebra, and statistics.

Many students ask if mathematics is important for a data science career, and we say yes. You can be a functional data scientist with mathematics, but you will need more mathematics knowledge to advance your career and gain domain expertise.

You will learn how a data scientist can analyze consumer behavior based on the data. Most companies use data analytics to learn more about user intent and send out tailor-made ads based on user data. In an age when personalization is the key to running a business, data analytics helps companies acquire and retain new customers.

You will learn the following:

• What is data analytics?
• Components of data analytics
• Types of data analysis: Qualitative and Quantitative

This module will teach you to play around with EXCEL and use it to its full potential for data storage, analysis, and visualization.

Along with this, you will learn how to visually represent data for novices or non-technical people. As a data scientist, your job will not end with finding patterns, predicting business insights or customer insights, and creating 360-degree user profiles. You will also need the skills to visually represent your findings through charts, graphs, heatmaps, etc. This module will train you to visually represent your data in the most optimum manner.

You will learn the following:

• Types of visual representations
• Techniques for data visualization
  • Data visualization using Tableau
  • Data visualization using Google Data Studio
  • Data visualization using Power BI
• Advantages and disadvantages of data visualization

In this module, you will learn the concepts related to Relational Database Management System [RDBM] and ETL [Extract, Transform and Load]- a program designed to create, update, and manage relational data. You will learn how an ETL tool extracts data from different RDBMs, transforms it and loads it to the data warehouse system. In most cases, this happens on an SQL interface which you will understand through this module.

Simultaneously, you will learn about Cloud Computing and its connection to RDBM. Cloud computing enables data analysis by sharing different resources and information across devices based on an internet connection. This makes cloud computing more like a Cloud Database Management System that acts through the cloud.

Using data to make accurate predictions is one of the core roles of a data expert. You will learn how to build a predictive model based on the organization’s historical data and known results using programming languages like Python. These models have the power to predict future events accurately, helping businesses devise more data-oriented strategies within specific conditions.

You will learn how to read data patterns and trends and use them to build predictive models.

This module will train you to use machine learning in real-world scenarios. You will learn how machine learning is applied across various domains and industries and the best Python libraries for machine learning.

You will become familiar with different types of machine learning, like supervised learning and algorithms, including time series forecasting, that helps improve forecasting accuracy while minimizing the loss function.

In this module, you will learn about the next type of machine learning, i.e., unsupervised machine learning. You will understand the concepts of clustering, Principal Component Analysis (PCA), and Recommendation Systems.

Throughout this module, you will understand how data interpretation is made, what methodologies are used to analyze data, and how AI is implemented to create a system that can make accurate product recommendations based on user search behavior or previous browsing patterns.

This module will introduce to you the concepts of Natural Language Processing and Text Mining using one of the most popular programming languages – Python. NLP and text mining are different concepts – NLP analyzes text, speech, or grammatical syntax to understand human language. Text mining, on the other hand, extracts information from structured and unstructured content. It has more focus on the content structure than meaning.

You will learn the machine learning algorithms used in text mining and NLP, along with techniques and methodologies to write your own data analysis algorithms using Python.

This module will introduce you to the various data analytics applications across different functions, like marketing, risk, operations, digital, etc.

• Marketing: You will learn to evaluate marketing activities based on data. You will learn how businesses use analytical processes to evaluate customer-based data and design solutions accordingly. You will also learn the three major types of marketing analytics – descriptive (which tells what has already happened), predictive (which predicts what could happen), and prescriptive (which tells what should happen in the future).

• Risk Management: Risk analytics is probably one of the most sought-after skills among companies looking for skilled data scientists. You will learn how to measure, assess, and manage risk by analyzing available data. You will learn to analyze data and predict business risks with maximum accuracy. This module will train you to understand patterns in data and trends and do competitor analysis – you will gain the eye to detect risks and be capable of mitigating them and bringing things under control.

• Operations: You will learn to make better business decisions with data. With data-collecting technologies evolving and firms investing in having informed business decisions, this module will prepare you to focus on data, align it with the profit and supply margins of the business, and deploy various business settings to improve overall business efficiency. You learn to predict outcomes, design models based on future demands and uncertainties, and also get introduced to frameworks and ideas that show how real businesses function.

This elective module will give you more clarity on the core concepts and their differences. You will understand how AI and Deep learning are part and sub-part of Machine Learning and how these concepts impact our daily lives on a micro and macro level. You will learn how:

• The concept of artificial intelligence lies in creating smart and intelligent machines.
• Deep Learning is a more advanced and complex concept where machines are trained with more complicated and vast datasets to replicate the thinking of a human brain. Simultaneously, you will understand how it uses artificial neural networks to mimic a human brain’s learning process.

You will learn about:

• Computer Vision: A field in AI that helps computers and systems to derive meaning from digital images, visual inputs, or videos and accordingly make accurate recommendations based on the acquired information.
• Text Mining: Using NLP, you will learn the methodologies and techniques to transform unstructured data into a structured format and identify meaningful patterns in it.

This is a second option to choose an elective specialization. Along with the data volume, data velocity has reached an all-time high. In this module, you will understand how big data refers to data that cannot be stored, processed, and analyzed using old-school methods. This module will cover frameworks like Hadoop, Cassandra, Apache Storm, and Spark and databases like NoSQL. You will also learn how to handle all this data: analyze it and derive meaningful insights from it.

You will work under the guidance of your mentor/teacher to complete your dissertation, based on which you will be reviewed. There is no pass/fail concept; however, an incomplete dissertation or incorrect project work will lead to failure in completing the program.

You can opt for a more practical approach in your final term through Capstone Project work. You will get an option to choose from multiple project options:

1. Sports event analysis and reporting
2. Consumer electronics pricing data analysis & visualization
3. Telcom churn prediction (Classification & Machine Learning)
4. Predicting credit card spending (Regression Methods)
5. Peer group lending analysis & prediction (Regression Methods)
6. Marketing & Sales data manipulation and analysis
7. Airlines data analysis and reporting
8. Sports equipment retail data analysis and visualization
9. Peer group lending analysis & prediction (Regression Methods)

You will learn the most important skill to succeed as an accomplished professional: Problem-solving. Your mentor will help you develop an approach that helps to break down and structure any complex problem into small logical steps/ tasks. And how to take a data-driven approach for robust business outcomes.

You will have one-on-one counseling on career development, resume reviews, job applications, and interview preparations from your mentors.

AnalytixLabs is a reliable name when it comes to placement and interview guidance. This program is no exception. You will get continuous support till you are placed in your dream company.

Get personalized support to prepare for interviews, a project portfolio, and your profile. Dedicated mentors and experts will guide you in this last module to understand which job role suits you best, how to approach that role, how to prepare for the interview, and any other guidance you need to get placed. Our placement support includes the following:

• Project Portfolio
• Profile building
• Mock interviews
• Career Guidance
• Internships / Freelance projects