Course Overview
Course Overview
In today’s data-driven world, every major business relies on data to make smart and profitable decisions. The Data Analyst Course is the perfect gateway to entering one of the highest-paying tech industries. Through this comprehensive program, you will learn how to collect, clean, analyze, and visualize complex datasets to solve real-world business problems. Technoglobe delivers this highly industry-oriented training, guiding you from absolute basics to advanced analytics tools. This course is heavily focused on hands-on practical learning so that every student can confidently work on live enterprise data.
Data Analyst Course Modules
Comprehensive Curriculum Overview
01
Introduction to Data Analytics & Statistics
02
Advanced Microsoft Excel for Data Analysis
03
SQL Fundamentals & Database Management
04
Advanced SQL Queries & Optimization
05
Data Visualization with Power BI
06
Data Visualization with Tableau
07
Python Programming Fundamentals
08
Python for Data Science (NumPy & Pandas)
09
Data Cleaning & Wrangling Techniques
10
Exploratory Data Analysis (EDA)
11
Introduction to Predictive Modeling & ML
12
Capstone Project & Analytics Career Track
Syllabus
Detailed Topic-wise Curriculum
Module 1: Introduction to Data Analytics & Statistics
- Role of a Data Analyst vs Data Scientist vs Data Engineer
- Descriptive vs Inferential Statistics
- Measures of Central Tendency (Mean, Median, Mode)
- Understanding Variance, Standard Deviation, and Outliers
- Probability Distributions and Hypothesis Testing
🛠️ Mini Case Study: Analyze market survey data to find trends using basic statistical metrics.
Module 2: Advanced Microsoft Excel for Data Analysis
- Cell Referencing, Data Sorting, and Multi-level Filtering
- Logical & Lookup Functions (VLOOKUP, HLOOKUP, INDEX-MATCH, XLOOKUP)
- Mastering Pivot Tables & Pivot Charts for Summarization
- Data Validation, Conditional Formatting, and Text-to-Columns
- Introduction to Power Query in Excel
🛠️ Practical: Build an interactive HR/Sales Performance Dashboard purely in Excel.
Module 3: SQL Fundamentals & Database Management
- Introduction to Relational Databases (RDBMS)
- SQL Architecture, Data Types, and Operators
- DDL Commands (CREATE, ALTER, DROP)
- DML Commands (SELECT, INSERT, UPDATE, DELETE)
- Filtering Data using WHERE, IN, BETWEEN, and LIKE clauses
📝 Practical: Design and create a relational database for an E-commerce system.
Module 4: Advanced SQL Queries & Optimization
- Mastering Joins (INNER, LEFT, RIGHT, FULL OUTER)
- Aggregate Functions (SUM, AVG, COUNT) & GROUP BY / HAVING
- Subqueries, Nested Queries, and Co-related Queries
- Window Functions (ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG)
- Common Table Expressions (CTEs) & Views
📝 Practical: Run advanced analytical queries to extract monthly revenue metrics from a live server dataset.
Module 5: Data Visualization with Power BI
- Power BI Desktop Workspace & Architecture
- Connecting to Various Data Sources (Excel, SQL Database, Web)
- Data Modeling: Star Schema, Snowflake Schema, and Relationships
- Introduction to DAX (Data Analysis Expressions) Measures and Calculated Columns
- Creating Charts, Slicers, Maps, and Custom Visuals
🛠️ Project: Build an automated, interactive Financial Performance Dashboard with Power BI.
Module 6: Data Visualization with Tableau
- Tableau Interface, Dimensions vs Measures
- Data Joins, Blending, and Unions in Tableau
- Creating Advanced Visualizations (Heat Maps, Bullet Graphs, Gantt Charts)
- Calculated Fields, Table Calculations, and Parameters
- Storyboarding and Interactive Dashboard Action Triggers
📝 Practical: Generate a Business Strategy Storyboard using Global Supply Chain Data.
Module 7: Python Programming Fundamentals
- Setting up Python with Anaconda & Jupyter Notebook
- Variables, Core Data Types (Integer, Float, String, Boolean)
- Python Collections: Lists, Tuples, Dictionaries, and Sets
- Control Flows (If-Else Conditions) & Loops (For, While)
- Defining Custom Functions and Exception Handling
📝 Practical: Write custom algorithms to automate text filtering tasks.
Module 8: Python for Data Science (NumPy & Pandas)
- Introduction to NumPy: Arrays, Vectorized Operations, Indexing
- Pandas Data Structures: Series and DataFrames
- Reading Data from Files (CSV, Excel, JSON) using Pandas
- Slicing, Filtering, and Subsetting DataFrames
- Grouping Data, Merging/Joining DataFrames, and Pivot Summaries
📝 Practical: Perform matrix calculations and large-scale dataset merges using Pandas syntax.
Module 9: Data Cleaning & Wrangling Techniques
- Identifying and Handling Missing Values (Imputation techniques)
- Detecting and Treating Duplicate Rows and Outliers
- Data Type Conversion and Standardizing String Format Text
- Regular Expressions (Regex) basics for Pattern Matching
- Handling Date-Time Objects effectively in Pandas
📝 Practical: Clean a heavily corrupted, unformatted unstructured dataset into production-ready data.
Module 10: Exploratory Data Analysis (EDA)
- Data Visualization libraries in Python: Matplotlib & Seaborn
- Univariate Analysis: Histograms, Box Plots, Density Charts
- Bivariate and Multivariate Analysis: Scatter Plots, Pair Plots
- Correlation Matrices & Heatmaps to discover hidden data relationships
- Feature Engineering Basics for extraction of key performance parameters
Module 11: Introduction to Predictive Modeling & ML
- Supervised vs Unsupervised Learning Overview
- Linear Regression Concept for Continuous Metric Prediction
- Logistic Regression & Classification Fundamentals
- Evaluating Models: R-squared, Mean Squared Error, Confusion Matrix
- Using the Scikit-Learn Python library
📝 Practical: Build a basic predictive script to estimate real-estate pricing trends.
Module 12: Capstone Project & Analytics Career Track
- End-to-End Analytics Capstone Project: Cleaning + Querying + Dashboard Storytelling
- GitHub & Portfolio building for Data Analysts
- Optimizing LinkedIn Profile & Resumes for Tech Interviews
- Mock Technical Interviews and Analytics Business Case Presentations
🚀 Final Target: Corporate Data Analyst Job Placement & Freelancing Setup Guidance
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What you’ll learn
- Become a UX designer.
- You will be able to add UX designer to your CV
- Become a UI designer.
- Build & test a full website design.
- Create your first UX brief & persona.
- How to use premade UI kits.
- Create quick wireframes.
- Downloadable exercise files
- Build a UX project from beginning to end.
- Learn to design websites & mobile phone apps.
- All the techniques used by UX professionals
- You will be able to talk correctly with other UX design.
admin
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admin
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