Data Analyst Course: Analytics Tools and Techniques
Introduction
This 3-month data analyst course is designed to provide learners with a strong foundation in modern data
analytics. Whether you choose to study online or offline, this course will help you explore the full data
lifecycle — from data collection methods to visual reporting and machine learning basics.
With a focus on practical learning, industry-standard Analytics Tools, and hands-on projects, you’ll gain the
skills needed for high-demand data analyst jobs. The course also prepares you for recognized data analytics
certification opportunities, making you job-ready in just three months.
Modules
Module 1
Course Papers
- Foundations of Data Analytics
Course Syllabus
- What is Data Analytics
- Why is Data Analytics Important
- Types of Data Analytics
- The Role of a Data Analyst
- Tools Used by Data Analysts
- Real-Life Applications of Data Analytics
- Career Path & Opportunities for Data Analysts
- Introduction to Data
- Types of Data
- Sources of Data
- Data Collection Techniques
- Overview of Data Analysis Tools
- Excel for Data Analysis
- SQL for Managing Databases
- Python for Data Analysis (Intro level)
- Tableau and Power BI for Data Visualization (Basics only)
- Introduction to Data Cleaning
- Importance of Data Cleaning
- Common Data Issues and Their Solutions
- Steps in Data Cleaning Process
- Tools for Data Cleaning (Excel, Python basic libraries)
- Best Practices for Data Cleaning
- What is Exploratory Data Analysis (EDA)
- Importance of EDA in Data Analysis
- Key Steps in Exploratory Data Analysis
- Tools for Performing EDA (Excel, Python – pandas/matplotlib overview)
- Best Practices for EDA
- Introduction to Data Visualization
- Importance of Data Visualization
- Types of Data Visualizations (Bar, Pie, Line, Scatter)
- Tools for Data Visualization (Excel, Power BI basics)
- Best Practices for Data Visualization
- Introduction to Data Preparation
- Importance of Data Preparation
- Steps in Data Preparation (Data structuring, missing value handling, formatting)
- Introduction to Statistics in Data Analysis
- Descriptive Statistics (Mean, Median, Mode, Standard Deviation)
- Inferential Statistics (Sampling, Basic Probability)
- Hypothesis Testing (Conceptual intro)
- Correlation and Regression (Basic overview)
Module 2
Course Papers
- Data Analysis & Machine Learning
Course Syllabus
- Introduction to Data Analysis Techniques
- Descriptive Analysis
- Diagnostic Analysis (Simple real-life example)
- Predictive Analysis (Regression overview)
- Introduction to Data Reporting and Visualization
- Types of Data Reports
- Data Visualization Techniques (As per real-world dashboards)
- Tools for Data Reporting (Excel, Power BI)
- Best Practices for Data Reporting and Visualization
- Example Use Case: Data Reporting in Retail (Summarized case only)
- What is Machine Learning
- Types of Machine Learning
- Common ML Algorithms (Linear Regression, Decision Tree – conceptual only)
- Steps to Perform Machine Learning (High-level workflow)
- Real-world Applications (Retail, Healthcare – examples only)
- Exercise: Data Cleaning and Preparation
- Exercise: Descriptive Statistics
- Exercise: Data Visualization (Excel or Tableau)
- Exercise: Predictive Analysis Using ML (Demo model only)
- Exercise: Real-world Problem Solving (Mini project)
- Exercise: Building Dashboards
- Exercise: Ethical and Legal Compliance Checklist (brief intro only)
Why Choose This Course?
Whether you're switching careers or upskilling, this course offers a practical and industry-ready roadmap to
become a professional data analyst. You'll gain hands-on exposure to popular big data tools and learn how
analytical big data can drive decision-making in real-world scenarios. With access to both offline training and
online data analyst course formats, flexibility meets quality education.
What Will You Learn?
- Data collection methods and tools
- Big data analytics and visualization using Power BI and Tableau
- Basics of Hadoop and big data types
- Descriptive and predictive analytics
- Introductory machine learning and deep learning concepts
- Building dashboards and analytical reports
Opportunities After This Course
- Data Analyst
- Business Intelligence Analyst
- Junior Data Scientist
- Reporting Analyst
You’ll be prepared to work in industries such as finance, healthcare, retail, and e-commerce, or pursue
freelance and internship opportunities in data analysis and reporting.
Who Can Enroll?
- Fresh graduates and final-year students
- Professionals looking to shift to data roles
- Anyone searching for a data analyst course near me
- IT and business background learners interested in analytics
Enroll Now
Enroll in our 3-month data analyst course today and step confidently into a world of insights, analytics, and
decision-making. Available both online and offline, this program offers everything you need to become
industry-ready.
👉 Start your journey in data analytics—Enroll Now!
