Data Analytics Course Training

Our Training on Data Analytics encompasses a broad range of technologies and methodologies aimed at enabling learner(s) to perform tasks that typically require human intelligence. To gain a comprehensive understanding of Data Analytics , consider exploring the following structured training modules:

Learning Objectives

By the end of this module, students will be able to:

• Understand the basics of data analytics and its applications.

• Use key tools such as Excel, SQL, Python, and Tableau for data analysis.

• Perform data cleaning and preprocessing.

• Apply statistical methods and data visualization techniques.

• Interpret data insights and make data-driven decisions.

Minutes-by-Minutes Breakdown

Part 1: Introduction to Data Analytics - (30 Minutes)

• What is data analytics?

• Types of data analytics (Descriptive, Diagnostic, Predictive, Prescriptive)

• Importance of data in business and decision-making

• Overview of tools and technologies

Part 2: Data Collection & Cleaning - (1 Hour)

• Data sources and types

• Data collection techniques

• Data cleaning processes: Handling missing values, outliers, and inconsistencies

• Introduction to data wrangling with Excel and Python (Pandas)

Part 3: Exploratory Data Analysis (EDA) - (30 Minutes)

• Understanding data distributions

• Summary statistics and measures of central tendency

• Data visualization basics (Histograms, Box plots, Scatter plots)

• Using Python (Matplotlib, Seaborn) and Excel for EDA

Part 4: SQL for Data Analytics  - (30 Minutes)

• Introduction to databases and SQL

• Writing basic queries (SELECT, WHERE, GROUP BY, ORDER BY)

• Joins and aggregations

• Case study: Extracting insights from a database

Part 5: Statistical Analysis & Hypothesis Testing - (2 Hour )

• Basic statistical concepts (Mean, Median, Standard Deviation)

• Probability and distributions

• Hypothesis testing (t-tests, chi-square tests)

• Case study: Applying statistical methods to real-world data

Part 6: Data Visualization & Reporting - (30 Minutes)

• Principles of effective data visualization

• Creating dashboards in Tableau and Power BI

• Storytelling with data

• Case study: Creating an interactive report

Part 7: Machine Learning Basics for Data Analytics - (1 Hour)

• Introduction to supervised vs. unsupervised learning

• Regression and classification models

• Hands-on session with Scikit-learn

• Model evaluation and performance metrics

Part 8: Capstone Project & Final Assessment - (1 Hour)

• Working on a real-world dataset

• Applying end-to-end analytics workflow

• Presenting findings and insights

• Final assessment and feedback session

Assessment Methods

• Weekly quizzes (20%)

• Hands-on assignments (30%)

• Capstone project (40%)

• Participation & discussion (10%)

Tools & Software Used

• Microsoft Excel / Google Sheets

• Python (Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)

• SQL (MySQL, PostgreSQL, or SQLite)

• Tableau / Power BI

Prerequisites

• Basic understanding of mathematics and statistics

• No prior programming experience required, but familiarity with Excel is beneficial

Certification

Students who successfully complete the module will receive a Certificate of Completion in Data Analytics.