Course Description
This comprehensive course is designed to equip students with the knowledge and skills necessary to perform advanced data analytics using a variety of techniques and tools. Students will gain hands-on experience in data manipulation, visualization, and interpretation, as well as learn how to apply statistical and machine learning methods to extract meaningful insights from complex datasets. .
What you’ll learn
- Using Python for Data Analytics
- Data Analysis with NumPy
- Data Manipulation with pandas.
- SQL for Data Analytics.
- Power BI Essentials
- Tableau
- Excel
Data - Data and it's types, data formats ,data sources and datasets
Data Analytics - Data Analytics, types of data anlytics, steps in data analytics
Data Collection - Primary data collection and secondary data collection, primary data collection using questionaire, webscraping and export data from databse to excel
Data Cleaning - Data cleaning using excel, outliers, scaling data and excel functions
Data Visualization - Basic data visualization using excel
Introduction to Python - Overview of Python programming language Setting up python development environments
Fundamentals of Python - Understanding Statements, Expressions and Indentation, Overview of Identifiers, Keywords and Comments Variables, Common Data Types, Type Casting and Conversion, Operators in Python, Hands-on Activity
Loops, Functions & Error Handling - Conditional statements, Looping constructs, Loop Control Statements: Break, Continue and Pass Defining and Calling Functions, Function Parameters and Return Values, Recursive Functions Introduction to Exception
Data Structures in Python - Lists & Manipulation Techniques, Slicing and Indexing in Lists, Tuples & Operations on Tuples, Slicing And Indexing in Tuples Common Operations on Both Lists and Tuples, Dictionaries & Manipulating Dictionaries, Sets & Manipulating Sets Common Operations on Both Dictionaries and Sets, Hands-on Activity
Introduction to Object-Oriented Programming - Classes and objects, Attributes and methods, Constructor and destructor methods, Inheritance and polymorphism
File Handling, Working with Libraries and Packages in Python - Reading from and writing to files, File modes and operations, Handling file exceptions, Introduction to external libraries and packages, Using pip for package management, Installing and importing libraries
Introduction to NumPy - Overview of NumPy and its features, Installing NumPy and setting up the development environment, Introduction to NumPy arrays: creation, indexing, and slicing, Creating multidimensional arrays
Array Manipulation - Reshaping and resizing arrays, Stacking and splitting arrays, Broadcasting and vectorization
Mathematical Operations - Element-wise operations, Universal functions (ufuncs) in NumPy, Aggregation and reduction operations
Data Analysis with NumPy - Loading and saving data using NumPy, Statistical analysis with NumPy, Data manipulation and filtering
Introduction to pandas - Overview of pandas and its benefits, Installation and setup, Introduction to pandas data structures: Series and DataFrame
Data Manipulation with pandas - Creating pandas Series and DataFrames, Indexing and selecting data, Filtering and sorting data
Data Cleaning and Preprocessing with pandas - Handling missing values: dropping, filling, interpolation, Removing duplicates, Data transformation and reshaping
Data Aggregation and Grouping - Grouping data with pandas, Aggregation functions: sum, mean, count, etc, Combining and merging datasets
Time Series Analysis & Visualizations with pandas - Data visualization using Matplotlib (basic plots, customization, EDA) Handling time series data with pandas, Resampling and frequency conversion, Time-based indexing and slicing
Univariate Analysis with pandas - Descriptive statistics: mean, median, mode, variance, standard deviation Distribution analysis: histograms, density plots, box plots, Summary statistics and visualization for single variables
Multivariate Analysis with Pandas - Correlation analysis: Pearson correlation, Spearman correlation, Scatter plots and pair plots for exploring relationships between variables, Heatmaps for visualizing correlation matrices
Working with SQL - What is SQL? Difference Between RDBMS and DBMS, Difference between SQL and NoSQL, Setting Up the Environment (MySQL), SQL Basics - DDL Commands, DML Commands, DQL Commands, String Operations, Ordering the display of tuples, SET Operations, Aggregation in SQL, Grouping in SQL, HAVING, EXISTS, ANY, CASE, SubQueries, Set Membership, Performing Join Operations
Querying SQL Databases with Python - Stored procedures, Executing SQL queries from Python
Introduction to Power BI - Overview of Power BI, Installation and setup, Understanding the Power BI interface, Introduction to Power BI Desktop.
Data Importing and Transformation - Connecting to various data sources (Excel, databases, etc.), Data loading and transformation using Power Query Editor Cleaning and shaping data for analysis
Data Modeling - Introduction to data modeling concepts, Creating relationships between tables Implementing calculated columns and measures
Visualizations, Dashboards and Report Creation - Overview of data visualization principles, Creating basic charts (bar, line, pie, etc.), Using advanced chart types (treemaps, waterfall, etc.) Formatting visualizations, Designing interactive dashboards, Publishing and sharing reports on Power BI Service Managing access and permissions
Data Analysis with Power BI - Introduction to DAX (Data Analysis Expressions) Writing DAX formulas for calculated columns and measures Performing data analysis using DAX functions
Case Studies - Analyzing Customer Churn, Competitor Sales Analysis, Analyzing Healthcare Data
Introduction to Tableau - Overview of Tableau and its features, Installation and setup, Understanding the Tableau interface Introduction to Tableau Desktop and Tableau Public
Data Connection and Preparation - Connecting to various data sources (Excel, databases, etc.), Data loading and preparation in Tableau Cleaning and reshaping data for analysis
Visualizations and Dashboard Creation - Creating basic charts (bar, line, pie, etc.) in Tableau, Formatting and customizing visualizations Implementing basic calculations and parameters, Using advanced chart types (treemaps, heat maps, etc.) Implementing dual-axis and combined charts, Creating interactive dashboards with actions and filters, Designing dashboards for different audiences and purposes
Data Analysis with Tableau - Introduction to Tableau calculations (Table calculations, LOD expressions), Using calculated fields for advanced analysis Performing statistical analysis and forecasting in Tableau.
Geographic Analysis - Visualizing geographic data in Tableau (maps, spatial joins, etc.) Customizing maps and adding layers Analyzing spatial patterns and trends
Case Studies - HR Analytics, Analyzing Job Marketing Data, eCommerce Analysis
Introduction to Excel for Data Analytics - Overview of Excel as a data analysis tool, Introduction to Excel interface and navigation, Understanding worksheets, rows, columns, and cells
Data Importing and Cleansing - Importing data from different sources (text files, CSV, databases, etc.) Cleaning and preprocessing data using Excel's built-in tools (text to columns, find and replace, etc.)
Data Transformation and Preparation - Using Excel's functions and formulas for data transformation (IF, VLOOKUP, INDEX-MATCH, etc.) Combining and restructuring data using PivotTables
Data Visualizations - Creating basic charts (bar, line, pie, etc.) in Excel, Customizing and formatting charts for effective visualization Building interactive dashboards using Excel features (Slicers, PivotCharts, etc.)
Advanced Data Analysis - Performing advanced calculations using array formulas Using Excel's statistical functions for descriptive analysis (mean, median, standard deviation, etc.) Conducting regression analysis and trend forecasting in Excel
PivotTables and Power Pivot - Advanced data analysis with PivotTables and PivotCharts, Introduction to Power Pivot for data modeling and analysis Creating relationships and measures in Power Pivot
Case Studies - Supply Chain Analysis, Sales Data Analysis
Google Datastudio - Google Datastudio -advantages & disadvantages
Create Report - choosing a template, connecting datasources, choosing metrics that matter,sharing reports.
Analyzing Job Marketing Data
Competitor Sales Analysis
Analyzing Healthcare Data
Project - Do a Project using the learned skills and present the output
Our Student Reviews
4.5
(Based on todays review)
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1 days ago
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Frequently Asked Questions
This course is ideal for students, professionals, and anyone interested in a career in data analytics. It's perfect for aspiring data analysts, business analysts, or professionals looking to enhance their data skills for better decision-making.
No prior experience is required to enroll. The course is designed for both beginners and professionals. However, a basic understanding of statistics and Excel will be beneficial.
Yes, IPSR offers placement assistance until you are placed. We work closely with top companies to help you find the best career opportunities in data analytics.
Yes, the Data Analytics course is available both online and offline. Our offline training is offered at our campuses in Kochi, Ernakulam, Calicut, and Kottayam.
Yes, upon successful completion of the course, you will receive an industry-recognized Data Analytics Certification from IPSR, which will enhance your career prospects.
After completing the course, you can pursue roles such as Data Analyst, Business Analyst, Data Scientist, or Analytics Consultant. Data analytics is a rapidly growing field with numerous opportunities across various industries.
5hr
2hr