Data Analytics Online Course

₹24999 ~~₹ 30000~~

17% off

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This includes following

100 Videos
100 Chapter
Duration : 4 Month
250 Hours
Completion certificate : Yes
Language : English

LIVE TRAINING + 100% PLACEMENT ASSISTANCE PROGRAM

ABOUT THE COURSE

This comprehensive data analytics course is designed to provide students with a foundation in the key concepts and tools used in data analysis. The course is ideal for students who want to learn the latest techniques and technologies for working with data and applying statistical analysis to real world problems.

Throughout the course, students will learn how to use Python and SQL to perform data manipulation and analysis, how to use Excel to perform
statistical analysis, and how to use popular tools such as Tableau and Power BI to visualize and present data insights.
The course is divided into five modules, each focusing on a different aspect of data analytics:

Module 1: Python Fundamentals
Module 2: SQL Fundamentals
Module 3: Excel for Data Analysis
Module 4: Data Visualization with Tableau and Power BI
Module 5: Statistics for Data Analytics

Each module includes mix of all live sessions to reinforce key concepts and skills, interactive exercises along with live doubt clearing sessions. Additionally, each module includes live projects and assessments to give students hands on experience with real-world data analysis problems.

Upon completion of the course, students will have a solid understanding of the core concepts and tools used in data analytics and will be equipped with the skills and knowledge needed to work on complex data analysis projects.

WHY DATA ANALYTICS?

Data analytics refers to the process of examining, transforming, and interpreting data to uncover meaningful patterns, trends, and insights. It
involves using various analytical techniques, tools, and algorithms to extract valuable information from datasets.

Data analytics focuses on understanding past and current data to gain insights that can drive decision-making and improve business
performance. It involves several stages, including data collection, data cleaning and preprocessing, exploratory data analysis, statistical
modeling, data visualization, and interpretation of results.

Data analytics is employed in various fields and industries, such as finance, marketing, healthcare, and manufacturing. It helps organizations
identify patterns, trends, and anomalies, optimize processes, improve efficiency, enhance customer experience, and gain a competitive edge.
Overall, data analytics empowers organizations to make data-driven decisions, optimize performance, understand customers, predict outcomes, gain a competitive edge, manage risks, and drive innovation.

By leveraging data effectively, organizations can unlock valuable insights and achieve their goals more efficiently and effectively.

Module 1: Python Fundamentals

Module 2: SQL Fundamentals

Module 3: Excel for Data Analysis

Module 4: Data Visualization with Tableau and Power BI

Module 5: Statistics for Data Analytics

Module 1: Python Fundamentals

Module 2: SQL Fundamentals

Module 3: Excel for Data Analysis

Module 4: Data Visualization with Tableau and Power BI

Module 5: Statistics for Data Analytics

  Module 1: Python Fundamentals (50 hours)
Introduction to Python
 Variables and data types
 Control flow statements
 Functions
Data Structures in Python
 Lists, tuples, and sets
 Dictionaries
 NumPy arrays
Data Cleaning and Preparation with Python
 Importing data from different file formats
 Cleaning and transforming data using pandas
 Handling missing and duplicate values
Data Analysis and Visualization with Python
 Data aggregation and summarization
 Visualizing data using Matplotlib and Seaborn
 Exploratory data analysis (EDA)

  Module 2: SQL Fundamentals (30 hours)
Introduction to SQL
 Basic SQL syntax
 Creating and managing tables
 Querying data using SELECT statements
Advanced SQL Concepts
 Joins and subqueries
 Filtering and sorting data
 Grouping and aggregating data
Data Manipulation with SQL
 Updating and deleting records
 Inserting new records
 Creating views

  Module 3: Excel for Data Analysis (30 hours)
Introduction to Excel
 Navigating the Excel interface
 Basic formulae and functions
 Formatting data
Data Manipulation and Analysis in Excel
 Filtering and sorting data
 Pivot tables and charts
 Using data validation and conditional formatting
Advanced Excel Features
 Using lookup functions
 What-if analysis using scenario manager and solver
 Macros and automation

  Module 4: Data Visualization with Tableau and Power BI (40 hours)
Introduction to Tableau and Power BI
 Navigating the interface
 Importing data
 Creating visualizations
Visualizing Data in Tableau and Power BI
 Creating different chart types
 Using filters and sorting
 Combining visualizations into dashboards
Advanced Features in Tableau and Power BI
 Using calculations and parameters
 Building interactive visualizations
 Integrating with other data sources

  Module 5: Statistics for Data Analytics
(30 hours)
Introduction to Statistics
 Descriptive vs inferential statistics
 Measures of central tendency and variability
 Probability distributions
Statistical Inference
 Hypothesis testing
 Confidence intervals
 P-values and significance levels
Correlation and Regression Analysis
 Simple and multiple regression
 Correlation coefficients
 Goodness of fit measures

HOW CAN CODINGSQUARE BOOST MY CAREER?

Codingsquare can boost your career through comprehensive learning, hands-on projects, industry-relevant curriculum, expert guidance, placement assistance, networking opportunities, and industry recognition. It equips you with the knowledge, skills, and support needed to succeed in the tech industry.

IS THIS A LIVE TRAINING OR RECORDED SESSIONS?

Participants and the teacher will log in at the same time for the live, interactive training, which will take place at a predetermined time. A recording of the same session will also be made, and viewers will have access to it to review, summarize, or watch a missed session.