Top-Rated Data Analytics Course

The first module of this data analytics course for beginners will cover the basics of Microsoft Excel. Students will learn data analysis essentials using Excel to create and format spreadsheets, along with CSV, tables, formulae, sorting, filtering, and much more.

• Why Excel? What are the advantages of Excel?
  Here, students will learn why Excel is a powerful spreadsheet application for analyzing and manipulating data and the advantages of using Excel for business and personal use.
• CSV File Format
  CSV files can be used with almost any spreadsheet program, such as Microsoft Excel, Apache Openoffice Calc, or Google Sheets. Here, students will learn how to use CSV for exchanging data between different applications.
• Tools, Ribbons, Commands
  In this, students will learn how to add functionality to a workbook and make working with data easier using tools, ribbons, and commands available in Excel.
• Cell Referencing
  This topic will teach students the process of cell referencing, a powerful feature in Excel that allows them to link data from multiple sheets and workbooks.
• Tables
  This topic will teach students how to implement tables in Excel to organize data and make it easy to view and understand.
• Basic Arithmetic Functions (+,-,*,/)
  This topic will make students familiar with implementing essential arithmetic functions to create more complex formulas that will unlock the power of Excel for data analysis needs.
• Date Functions
  This topic will familiarize students with implementing date functions using different formats in Excel.
• Sorting
  Here, students will learn how to sort data, where they can organize data in a way that makes it easier to find the information they need and to see relationships between different pieces of data
• Filtering
  Here, students will learn how to filter data, a powerful way in data analysis where they can easily view subsets of their data by hiding the rows that don't meet their criteria.
• IF ELSE
  The IF-ELSE function in Excel is a handy tool that allows us to perform different actions depending on whether a condition is met or not. This can be particularly useful when we have a large dataset and want to perform different analyses depending on specific criteria.

This module will cover the basic concepts of descriptive statistics, including measures of central tendency (mean, median, and mode) and measures of dispersion (range, variance, and standard deviation).

• Seeing patterns in the data
  Here, students will learn how to identify and analyze patterns in the data with the assistance of descriptive statistics.
• Sample and Population
  Here, students will gain an understanding of several concepts in probability, such as sample and population.
• Central Tendency (Mean, Median, Mode)
  This topic will make students familiar with measures of central tendency (mean, median, and mode) to help them calculate the average, find the median value of a dataset, and find the most frequent value.
• Dispersion (Range, Variance, Standard Deviation)
  This topic will make students familiar with measures of dispersion (range, variance, and standard deviation), which is essential for analyzing data sets because it can give us insights into the spread of the data.
• Five point Summary
  In this topic, students will understand the five-point summary in descriptive statistics.

Industry 4.0 is the term used to describe the fourth industrial revolution, and data is the lifeblood of Industry 4.0. In this module, students will explore the world of data and how data is critical for the industrial revolution.

• Introduction to Analytics Lifecycle
  In this chapter, students will go through the various phases involved in the data analytics lifecycle.
• Datasources and Databases
  Datasources are the information repositories that hold the data sets that analysts utilize to perform their work.
• A typical Data Pipeline
  This chapter will familiarize students with the data pipeline, a series of steps to ingest, transform and analyze raw data.
• Insight generation and Recommendation
  Here, students will familiarize themselves with the process of analyzing data to discover trends and patterns that can be used to generate new insights and make recommendations.
• End-to-end Business Case Study Demo
  Here, students will go through a hands-on demo of an end-to-end business case study.

In this module, students will explore the process of generating insights in multiple ways using Excel, such as tables, tabs, charts, and descriptive statistics.

• Pivot Tables
  This topic will make students understand pivot tables, which allow them to quickly summarize large amounts of data in a concise, easy-to-understand format.
• Sorting Data in Pivot Tables
  Here, students will learn how to sort data in pivot tables, where they can sort by values, by column, by row, and by multiple columns and rows.
• Filtering Data in Pivot Tables
  Here, students will learn how to filter data in pivot tables, where they can filter by date, product, customer, or any other entity.
• Analyse Tab
  Here, students will learn how to work with the analyze tab in Excel, which allows them to perform various statistical analyses on their data, like calculating means, standard deviations, percentiles, etc. They can also use the tab to create charts and graphs to visualize their data.
• Exploring charts
  In this topic, students will explore a variety of charts available in Excel to visualize data sets in multiple formats.
• Descriptive Statistics
  This chapter will help students analyze and understand diverse data sets in Excel with the aid of descriptive statistics.

Querying data with SQL allows us to find and manipulate data in our database quickly. In this module, students will learn how to write and understand SQL queries to retrieve data from any database.

• Importing a Database
  Here, students will learn the process of importing a database into MySQL.
• Introduction to RDBMS
  This topic will introduce students to RDBMS, a relational database management system to create, store, update, and delete data in a relational database.
• Selecting data
  When working with data stored in a MySQL database, it is often necessary to select specific data in order to work with it. Here, students will learn how to select data in a variety of ways using the SELECT statement.
• Filtering data
  When working with databases, it is often necessary to filter data to return only the rows that meet specific criteria. Here, students will learn how to filter data and make their queries more specific using the WHERE clause.

Advanced querying encompasses a variety of techniques that allow a user to manipulate data in order to answer complex business questions. In this module, students will learn the process of advanced querying to extract business insights.

• Aggregating data
  Students will get familiar with data aggregation in SQL, a process of combining data from multiple tables into a single table, where a calculation is performed on a set of values and returns a single value.
• Joining data
  Students will familiarize themselves with combining data from two or more tables into a single table using the JOIN command.
• Window Functions
  Here, students will learn how to identify values in a collection of rows and provide a single result for each row, which is called the window function.
• Order-of-Execution
  Students will be introduced to the order-of-execution technique, which defines the specific order in which the clauses, expressions, and operators in a statement are evaluated.
• Extracting data to Excel to perform data analysis

Once students are done with the fundamentals of data analytics, this data analysis course for beginners will provide students with the first hands-on project on the topics learned so far.

This module will give students a comprehensive introduction to the Python programming language, covering topics like Google Colab, variables, data types, data structures, conditional statements, loops, and functions.

• Setting up Google Colab
  Google Colab is a free notebook environment for writing and executing code. Students will learn how to set up and work with Google Colab in this section.
• Variables
  Here, students will learn how to work with variables in Python to store values and retrieve them later.
• Data Types
  Here, students will understand data types, which define the type of data that a variable can hold. There are several built-in data types in Python, including integers, floats, and strings, among others.
• Data Structures
  Python's standard library provides a wide range of data structures that can be used to store and efficiently organize data. The most commonly used data structures are lists, tuples, dictionaries, and sets.
• Conditional Statements
  This topic will familiarize students with conditional statements that help them execute the code only if the specified condition is met.
• Loops
  The concept of loops will be taught to the students in this chapter. Loops can execute a block of code continually until a specific condition is met, such as computing the sum of two integers or displaying multiplication or other tables, among other things.
• Functions
  This chapter will help students understand and use Functions using Python programming so that they may reuse code.

Numpy is a powerful library for performing numerical operations on arrays and matrices. At the same time, Pandas is a library for working with data frames, which are similar to tables in a relational database. In this module, we'll explore how to use these two libraries to perform various data transformation tasks.

• Numpy Arrays
  A Numpy array is a multidimensional array of objects of the same type, and this topic will teach students how to perform numerical operations efficiently using Numpy arrays.
• Numpy Functions
  This article will make students familiar with various Numpy functions that can assist them in speeding up their code.
• Indexing
  Students will learn how to find and retrieve data from a given data structure using Indexing in this topic.
• Accessing
  Here, students will learn how to access data from a Python project using the dot (.) operator.
• Pandas Series
  In this topic, students will understand how to hold several data types, such as numbers, strings, etc., using a one-dimensional array-like object, i.e., the Pandas Series.
• Pandas Dataframes
  Here, students will gain an understanding of Pandas Dataframes, which are two-dimensional, size-mutable, potentially heterogeneous tabular data structures with labeled axes (rows and columns).
• Saving Loading
  Here, students will explore the process of saving and loading files in multiple formats using the Pandas library.
• Merging dataframes
  This topic will familiarize students with the process of combining/merging two or more dataframes into a single dataframe with the help of specific methods.
• Pandas Functions
  This topic will familiarize students with various Pandas functions that are widely implemented in numerous applications of data science and machine learning.

Exploratory Data Analysis, also known as EDA, uses visual techniques to help us find patterns and insights frequently inside specific data. This module will explain EDA using Python in-depth.

• Data Sanity Checks
  This topic will make students understand the significance of performing sanity checks to ensure that the data is clean and ready for analysis while working with data.
• Univariate Analysis
  The students in this topic will gain an understanding of how to perform statistical comparisons using univariate analysis.
• Bivariate Analysis
  The students in this topic will gain an understanding of how to perform statistical comparisons using bivariate analysis.
• Missing Value Treatment
  This topic will familiarize students with the number of ways to deal with missing values when performing exploratory data analysis.
• Outlier Detection
  This topic will familiarize students with the number of ways to detect outliers that can help identify problems and patterns in data for further analysis.

Once students are done with the data analysis essentials, this data analyst course for beginners will provide students with the second hands-on project on the topics learned so far.

• ChatGPT - Overview 
• ChatGPT - Business Applications 
• Generative AI Demonstrations

In this course, you will learn how to read, explore, and effectively visualize data using Tableau and tell stories by analyzing data using Tableau dashboards. 

•  Storytelling with Data 
• Creating Interactive Dashboards

This module aims to equip participants with the skills to prepare data from various sources to harness the potential of Power BI for data analysis and visualization. Participants will start by exploring the Power BI interface and its features, learn how to connect to data from various sources, get introduced to basic data modeling concepts followed by Power Query for data preparation, and explore advanced data modeling techniques to get the data ready for analysis and deriving actionable insights from their data. 

 (PowerBI interface and features, Connecting to data from various sources, Creating visualizations and reports, Basic data modeling, Power Query for data preparation, cleaning, and transformation, Creating relationships between data tables, Advanced data modeling techniques)

This module delves into data modeling techniques within Power BI, equipping participants with the skills to design and implement efficient data models. Participants will explore various data modeling approaches in Power BI, explore designing data models using the star schema and other relevant data structures, utilize DAX (Data Analysis Expressions) commands to create sophisticated calculations and measures, and build comprehensive reports and visualizations using queries and analytical techniques, helping participants transform raw data into actionable insights and compelling visual narratives. 

 (Data modeling approaches in PowerBI, Designing data models using star schema and data structures, DAX commands for creating calculations and measures, Building reports and visualizations using queries and analytics)

This module focuses on advanced techniques in Power BI to enhance data analysis and model management. Participants will explore advanced time intelligence calculations using DAX functions, implement strategies for optimizing performance in Power BI models and reports, improve query performance with DirectQuery and variables, and gain insights into securing data access and controlling model objects, ensuring data integrity and compliance with organizational policies. 

 (Advanced time intelligence calculations using DAX functions, Optimizing performance in PowerBI models and reports, Improving query performance with DirectQuery and variables, Managing data complexity with cardinality reduction and aggregations, Securing data access and controlling model objects)

 This module provides participants with the knowledge and skills needed to effectively deploy and manage Power BI solutions within an organization. Participants will learn best practices for securing and protecting Power BI data and models, techniques for monitoring and troubleshooting Power BI performance and issues, implementing row-level security and data governance to control and manage data access, and creating and managing dashboards for data exploration and insights, empowering them to design interactive and insightful dashboards that facilitate informed decision-making and data-driven exploration. 

(Deploying and managing PowerBI solutions, Securing and protecting PowerBI data and models, Monitoring and troubleshooting PowerBI performance and issues, Implementing row-level security and data governance, Creating and managing dashboards for data exploration and insights)