Data Science Masters Program

In this module of the Data Science Masters Program, you will get deep insight into the data and various data types with the importance and Applications of Data-Science in today's data-driven world. The module will also cover the topics like Introduction to Databases and their types, Steps of Data Science & Machine Learning, and use cases of Data Science in different industries.

Here you will learn the basics of statistics, including descriptive and inferential statistics, variables and types of variables, measures of center and spread, measures of central tendency and dispersion, and the mean, mode, median, range, standard deviation, variance, quartile, IQR, covariance, and correlation between data. You will also learn how to create and interpret histograms. This module will provide a solid foundation for understanding statistical concepts and analyzing data.

In this sub-module, you will learn about inferential statistics, including the difference between sample and population, hypothesis testing, null and alternative hypotheses, type I and type II errors, establishing a rejection region and significance level, and the concept of p-values. You will also learn about t-tests, including one-sample and two-sample t-tests, as well as in-depth knowledge about ANOVA, including one-way and two-way ANOVA. Additionally, you will explore chi-square analysis and the differences between parametric and non-parametric tests. This module will provide a deeper understanding of statistical concepts and their practical applications in data analysis.

In this lesson, the learner will get a good glance at the basics of databases. Also, it will be a fantastic session that will involve understanding various types and components of the Database.

Once familiar with the database concept, you will be introduced to SQL. Understand the various types and functions of SQL like RDBMS-Relational Database Management System, Data types and functions, Creating Databases and Tables, Designing Your Database, etc

The operators are symbols used to carry out operations with values. These operators are used with SQL clauses such as SELECT, WHERE, ON, etc. In this lesson, you will learn the types of SQL Operators. SQL Comparison, logical, compound, and unary operators also gain experience with arithmetic operators.

This module will explain using different types of SQL, Join tables, and Variables queries to select data from multiple tables. The user will understand Update & Delete Query, SQL Joins-What is Joins, Inner Join, Left Join, Right Join and Full Join, Multiple Joins-Joining More than two tables, Hands-on Learning/Exercise, Create Schema in SQL, and more.

In this session, get ready to explore the methods of writing subqueries in SQL. You will be exposed to an Introduction to subqueries in SQL and applications, how to write Subqueries in SQL, and techniques to create and view subqueries.

Get to know in detail about the Views, Functions, and Stored Procedures in SQL, when and how to best use them. Learn more about How to create, modify, delete, drop, rename, alter,and manage views, what is Stored Procedures and their benefits, Work with stored procedures, Learning user-defined functions, and Hands-on Learning.

Get the complete overview of Data Science with Python in this module. This module will also explain how Python is used for Data Science applications, industries working with Python, and Applications of Python in different sectors with features and components of Python.

This lesson will help you connect with the Python environment with Python installation and setup process, you will also understand all about Python IDE working mechanism and Running some Python basic commands.

In this module, you will get hands-on experience using indentation like tabs, space, and built-in data types in Python. The module will also cover the primary operators and functions, conditional and control statements like if, else, break, continue, loops in Python-For, While, and more.

Expand your knowledge of NumPy in this session. Understand various NumPy functions like arrays and matrices, array indexing, array math, Inspecting a NumPy array, NumPy array manipulation, Basic Numpy operations and more.

This sub-module of Data Science Masters Program will guide you on how you can manipulate data with Pandas, get introduced to Pandas understand the data frame, series object in pandas, data frame in Pandas loading and handling data with Pandas.

This lesson provides an introduction to Machine Learning, covering the types of Machine Learning, modeling flow, and unsupervised and reinforcement learning. By the end of the module, learners will have a fundamental understanding of Machine Learning and its applications.

This lesson introduces Linear Regression and its different types, including Simple and Multiple Linear Regression, Lasso and Ridge Regression. Students will learn about performance metrics and implement the different regression techniques through assignments and hands-on learning. By the end of the lesson, learners will be able to apply Linear Regression to real-world data analysis problems.

This lesson is an introduction to Supervised Learning, covering real-life applications, flow, and different types of supervised algorithms. It also delves into specific algorithms such as Logistics Regression, Decision Tree, K Nearest Neighbour, Support Vector Machine, and Naive Bayes. The lesson emphasizes accuracy metrics, confusion matrix, and cost matrix. By the end of the lesson, learners will have a practical understanding of Supervised Learning.

This lesson is an introduction to Unsupervised Learning, covering real-life applications, flow, and different types of unsupervised algorithms. It also delves into specific algorithms such as Clustering, K-means Clustering, Hierarchical Clustering, and PCA Technique. Students will learn about accuracy metrics and have hands-on learning opportunities. By the end of the lesson, learners will have a practical understanding of Unsupervised Learning and its applications.

This lesson covers Ensemble Learning, which combines multiple machine learning models to improve performance and accuracy. It covers different ensemble learning algorithms, such as Random Forest, Bagging & Boosting, Ada Boost, Gradient Boosting, and Xg Boost. Students will also learn about model selection, common splitting strategies, and have opportunities for hands-on learning. By the end of the lesson, learners will have a practical understanding of Ensemble Learning and its applications.

This lesson is an introduction to Deep Learning and AI, covering the basics of AI and the differences between Machine Learning and Deep Learning. It delves into Real-life applications of Deep Learning and explores the comparison between the Human Brain and Artificial Neural Networks. The lesson also includes an introduction to TensorFlow and Keras.

This lesson od Data Science course covers an introduction to TensorFlow, including a "Hello World" program and how to use it for Linear Regression and Logistic Regression. The lesson also covers Deep Neural Networks and provides an assignment to reinforce learning.

This lesson covers Convolutional Neural Networks (CNN), including their design and architecture. It also covers Neural Style Transfer, Transfer Learning Method, and Famous CNN architectures. The lesson provides a knowledge check and an assignment to reinforce learning.

This lesson covers the architecture of RNN, Backpropagation in RNNs, and applications. The lesson also delves into the problems with RNNs and the need for LSTM, and provides an overview of Long short-term memory (LSTM). The lesson concludes with a knowledge check and an assignment to reinforce learning.

In this module, you will get introduced to Big Data, how hadoop solves the big data problem, characteristics, components and architecture of Hadoop, etc. Know more about the Apache Spark and learn why it is important. Study wide range of details on Data-Parallel to distributed Data-Parallel, Latency etc, followed by the knowledge checks and subjective assignments.

This module will cover the basic introduction of Spark data Frames with its basic, groupby and aggregate operations. Get good exposure on the missing data, dates & timestamps with collective knowledge check and assignment.

Get introduced to machine learning and ISLR, learn ML with spark and Python with MLib, along with linear and logistic regression. The module will explain details on decision tree, random forest and k means clustering along with exposure how to use spark for NLP, the knowledge checks and assignments will clear the concept of machine learning with mlib.

Tableau is one of the most influential and rapidly growing visualization software tools in data science. It helps clarify raw data into an easily understandable structure without requiring technical and coding knowledge. This module will light up your potential by focusing on tableau's core functions, including Installing Tableau, Tableau desktop vs. Tableau Public, the User Interface of Tableau Public, and Data Preparation.

It will be an exciting session to understand how various data sources are connected—the Connection to Text Files, the Connection to Excel File, and the Connection to the Database.

This session will let you play with metadata. Discover new data types possibilities and get the experience of working with metadata.

This session is about understanding the data operations that combine multiple datasets based on various fields and discovering new datasets. Get to know all about the join data. What are Joins? Relationships vs. Joins, Create a Join, Join types, Join Clauses, Null Values in Join Keys, Cross-Database Joins, and more.

Experience an impressive session and dive into the detailed insights of Data Blending. Understand What Blends are, Steps for Blending, Understand Primary and Secondary Data Sources, and Work Across Blended Data Sources, Null Values in Join Keys, and Cross-Database Joins.

In this lesson on Visual Analytics in Tableau, you will learn about various Tableau tools such as Marks Card, Highlighting, Sorting, Grouping, Sets, Bins, and Hierarchies, among others. The lesson will also include a knowledge check and an assignment to test your understanding and skills.

In this lesson, you will learn about different types of filters such as dimension, date, measure, visual, interactive, and context filters. You will also learn how to use these filters in Tableau to analyze and visualize data effectively.

In the module of data Science Pro Program, this particular session will cover the overview of creating charts in Tableau, Bar Chart, Histogram Highlight Tables, Maps, and other chart types.

After you learn about the basic chart, this session will introduce you to the world of advanced charts. You will be familiar with the Box and Whisker Plot, Bullet Chart, Bar in Bar Chart, and Step and Jump Lines. Bump Chart, Word Cloud, Donut Chart, and many more chart types

After the advanced charts this session will cover the LOD expressions and concepts, the syntax expressions along with the aggregation and replication with LOD expressions also the nested LOD expression.

Have an amazing learning experience with the dashboards. The building of dashboards with the layouts , formatting. Discover the interactive dashboards with actions with designing dashboards for devices with detail understanding in the story points. The session will also include the knowledge checks and related assignments.

Get introduced to the most interesting tool Power BI, get familiar with the use and crucial benefits of power BI. The various components and architecture of power BI will give you a great lerning experience. Understand the important aspect like building blocks of Power BI. The session will give you a knowledge check with assignment.

This lesson provides an overview of Power BI Desktop and its components including Data Sources, Query Editor, Report, Data and Model. It explains how to connect to data sources, use the Query Editor, and create reports using different visualizations.

This lesson covers how to clean and transform data using Query Editor in Power BI, as well as how to combine data by merging and appending. It also covers techniques for handling irregularly formatted data, empty rows, and formatting errors, and explains how to split and pivot data columns.

In the Data Modeling lesson, you will learn about how to model data and manage data relationships in Power BI Desktop. You will also understand the cardinality between tables such as many-to-one and one-to-one and how to set cross-filter directions and handle many-to-many relationships.

This lesson covers the introduction to M Query and its syntax, expressions, values, and operators. You will also learn about the Let expression and type conversion. The lesson includes creating a query with Query Editor and using simple Power Query M formula steps and functions.

The lesson on "DAX" introduces the concept of Data Analysis Expressions (DAX) and its importance in Power BI. It covers the syntax, data types, operators, functions, and filtering and evaluation context in DAX. It also includes creating simple and compound measures and DAX queries.

This lesson covers the topic of exploratory data analysis using Power BI. It includes using various Power BI charts, tables, matrices, slicers, and map visualizations. Students will also learn about custom visuals, KPI visuals, and Power View and Power Map. Other topics include modifying colors, shapes, and images, page layout and formatting, and visualization interaction.

The lesson on "Filters" covers the different types of filters in Power BI, including automatic and manual filters, as well as include and exclude filters. The lesson also includes information on drill-down, cross-drill, and drillthrough filters, as well as URL and pass-through filters.

The lesson on "Power BI Report Service" covers the basics of Power BI Report Server, including the web portal, paginated reports, and data gateways. It also includes information on scheduled refresh and how to set it up in Power BI.