Data Analytics
Unlock Your Potential with Industry-Recognized Data Analytics Certification
In today’s data-driven world, organizations rely on skilled data analysts to transform raw data into actionable insights. Our Data Analytics Certification from Techtroma is designed to equip professionals with the analytical, statistical, and technical skills required to excel in this dynamic field.
Why Choose This Certification?
- Industry-Recognized Certification – Gain a competitive edge with a globally accepted credential from Techtroma.
- Hands-On Learning – Work on real-world datasets, case studies, and industry projects.
- Expert-Led Training – Learn from seasoned data analysts and industry practitioners.
- Flexible Learning Modes – Online, classroom, and hybrid options to suit your schedule.
- Placement Assistance – Get career guidance and job support after certification.
What You Will Learn
Data Analytics Foundations – Understanding data types, data structures, and preprocessing.
Statistical Analysis – Descriptive & inferential statistics, hypothesis testing
SQL for Data Analysis – Writing queries, data extraction, and transformation.
Data Visualization – Using Power BI, Tableau, and Excel to present insights.
Machine Learning Basics – Introduction to predictive analytics and clustering techniques.
Python for Data Analytics – Hands-on coding with NumPy, Pandas, and Matplotlib.
Real-World Case Studies – Practical exposure to business use cases across industries.
Who Can Enroll?
- Aspiring Data Analysts & Business Analysts
- Professionals from IT, Marketing, Finance, and Operations
- Students and Freshers looking to enter the analytics domain
- Entrepreneurs & Business Owners seeking data-driven strategies
Course Curriculum
1.Introduction to Data Analyst
1.1 Introduction to Data Analyst
1.2 What is Data Analyst?
1.3 Why Data Analyst?
1.4 Cases for Data Analyst.
1.5 Data Analyst Classification.
2.Basic Python Programming.
2.1 Introduction
2.2 Python Data Types- int, Number, Boolean
2.3 String, Tuple, List, Dictionary and set
2.4 String Operation and Functions.
2.5 Tuple operation and Functions.
2.6 List operation and Functions
2.7 Set operation and Functions.
2.8 Dictionary operation and Functions.
2.9 If-else, elif ,Statement
2.10 Loops & Break and Continue Statement.
3.Basic Statistics
3.1 Introduction Statistics.
3.2 Data Types.
3.3 Central tendancy.
3.4 Dispersion.
4.Data Visualizations
4.1 Basic Statistics Contents.
4.2 Datasets.
4.3 Graph designing.
4.4 Filtering dataset
4.5 Mapping a dataset
5.Business Analytics Using Excel
5.1 Basics of Business Analytics.
5.2 Excel Conditional Formatting and Key Functions.
5.3 Data Analysis with Pivot Tables .
5.4 Creating Dashboards.
5.5 Advanced Business Analytics Using Excel.
6.Advanced Excel.
7.1 Writing conditional expressions (using IF).
7.2 Sorting and Filtering Data.
7.3 Data Validations.
7.4 Creating Pivot tables.
7.5 Date and time functions.
7.SQL Fundamentals.
8.1 Essential SQL Statements.
8.2 Database Backup and Restore Techniques.
8.3 Data Selection and Filtering.
8.4 Data Selection and Ordering
8.5 Advanced SQL Topics.
8.Power BI.
8.1 Introduction to Power BI.
8.2 Creating and managing interactive reports and dashboards.
8.3 Utilizing advanced features like Quick Insights and natural language queries.
8.4 Best practices for data layout, visualization, and collaboration using Microsoft Teams.
8.5 Example of Power BI.
9.Tableau.
9.1 Introduction Tableau.
9.2 Introduction to tableau and its core concepts.
9.3 Creating various types of visualizations, including charts and graphs.
9.4 Building interactive tableau dashboards and visual stories
9.5 Data blending and formatting techniques for effective visualization.
9.6 Examples.
10.Advanced-Data Visualization
10.1 Slicers and Filters in Power BI
10.2 Interactive Visualizations in Power BI
10.3 Creating Dashboards in Power BI
11.Data Cleaning & Transformation using Excel
11.1 Data Cleaning Techniques
11.2 Data Transform Techniques
11.3 Inbuilt Column and Row Transformations
12.Power Query
18.1 Introduction to Power Query
18.2 Data Types and Filters in Power Query.
18.3 Creating a Query in Power Query
13.Publishing Dashboards
20.1 Introduction to Power BI Service
20.2 Introduction of Dashboards.
20.3 Collaboration using Power BI
20.4 Creating Dashboards using Power BI Cloud/Service.
20.5 Publishing Your Dashboard
Data Analyst Project Life Cycle
Phase 1:Data Preparation Phase
This phase involves understanding business objectives, collecting data from various sources, and cleaning it to ensure accuracy. Exploratory Data Analysis (EDA) is conducted to identify patterns, trends, and anomalies, helping shape the analysis approach.
Phase 2:Data Analysis & Modeling Phase
Data is transformed and analyzed using statistical techniques, machine learning models, or business intelligence tools. Insights are derived through regression, clustering, or predictive modeling, ensuring alignment with business goals and validating hypotheses.
Phase 3:Interpretation & Reporting Phase
Findings are visualized using dashboards and reports to communicate insights effectively. Actionable recommendations are provided, followed by implementation and continuous monitoring to optimize strategies and ensure data-driven decision-making.