Data Analytics
Data Analytics is all about examining and interpreting data to derive meaningful insights. It involves collecting, processing, and analyzing data to help organizations make informed decisions such as identifying trends, predicting outcomes, and optimizing performance.
Data analysts use tools and languages like SQL, Python, PowerBI, Excel, and Tableau to extract, process, and visualize data effectively. Their goal is to uncover insights and trends that help organizations make informed decisions, ensuring data-driven strategies are implemented across various business functions.
If you’re passionate about working with data to uncover insights and drive decision-making, a career in data analysis could be the perfect fit for you!
What You’ll Learn
| Introduction to Data Analytics Definition and Importance of Data Analytics How Companies Leverage Data for Decision-Making Key Differences Between Data Analytics and Data Science Career Paths and Job Opportunities in Data Analytics Overview of the Four Types of Data Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive Understanding Data: Sources, Collection, and Storage Classification of Data: Qualitative vs. Quantitative, Structured vs. Unstructured The 4V’s of Big Data: Volume, Variety, Velocity, and Veracity The Data Lifecycle: Generation, Collection, Processing, Analysis, and Interpretation | SQL for Data Analysis Introduction to SQL, Databases, and Queries Application Dashboards & Schemas Creating Tables with SQL (Methods) SELECT, INSERT, UPDATE, DELETE Keywords Understanding Primary & Foreign Keys Data Import in SQL Using Operators, Indenting, Toggle, AND/OR/NOT Keywords IN, BETWEEN, LIKE, ALIAS, LIMIT, ORDER BY Keywords Aggregate Functions, GROUP BY, HAVING, ALIAS CASE Syntax, Types of Joins: Full, Left, Right, Inner 4th Portfolio Creation & Additions Project Phase IV – 3 Projects |
| Microsoft Excel for Data Analytics Core Principles and Rules of Data Analytics Data Visualization Techniques: Charts, Graphs, and Dashboards Using Pivot Tables for Dynamic Data Summarization Conditional Formatting for Data Highlighting and Analysis Lookup Functions: VLOOKUP, HLOOKUP, and XLOOKUP for Advanced Data Searches | Python for Data Analytics Introduction to Python Overview and Setup Jupyter Notebooks / Anaconda Installation Variables, Naming Conventions (CamelCase, PascalCase, Snake_case) String Concatenation, Arithmetic Operations, Multiple Variable Assignment Print Statements, String Merging with Variables |
| Data Analysis with Excel Core Principles and Rules of Data Analytics Data Visualization Techniques: Charts, Graphs, and Dashboards Using Pivot Tables for Dynamic Data Summarization Hands-on Practice: Two Weeks of Real-Life Data Analysis Project Phase I: 5 Hands-on Projects Using Excel | Data Types in Python Numeric Types: Integers, Floats, Complex Numbers Sequence Types: Strings, Lists, Tuples Boolean, Set, and Dictionary Data Types Best Practices for Data Types Indexing, Slicing, and Nested Lists Tuples (Immutable Sequences) and Sets |
| Business Intelligence with Power BI Introduction to Business Intelligence and Its Role in Data-Driven Decision Making Installing and Setting Up Power BI for Analytics Connecting Power BI to Various Data Sources (Excel, SQL, APIs, Cloud Data) Understanding Power Query and Query Editor for Data Manipulation Troubleshooting and Formatting Data in Power BI Append & Merge Queries, Text Transformations, and Numeric Calculations Advanced Power Query Functions for Data Cleaning and Preparation Building Dashboards and Interactive Reports in Power BI Project Phase II: 4 Hands-on Projects | Data Analysis with Pandas Introduction to Pandas Reading CSV, Excel, and Other Files Filtering Columns & Rows, Indexing in Pandas Group By & Aggregate Functions Data Type Conversions Merging DataFrames Creating Visualizations with Python Project Phase V : Python Project |
| Data Visualization with Tableau Introduction to Tableau and Its Applications in Data Analysis Understanding Measures and Dimensions in Data Visualization Formatting Tools and Techniques in Tableau for Better Insights Data Cleaning and Transformation in Tableau Creating Interactive and Dynamic Visualizations Filtering Data and Building Complex Dashboards Data Modeling Concepts, Cardinalities, and Joins in Tableau Using Calculated Fields for Advanced Data Computation Forecasting and Predictive Analytics in Tableau Creating and Presenting Data-Driven Stories in Tableau 3rd Portfolio Creation & Additions Project Phase III: 3 Hands-on Projects | Python Projects & Web Scraping Python Project 1: BMI Calculator Python Project 2: Web Scraping Basics (BeautifulSoup + Requests) Python Project 3: Scraping Data from a Real Website |
| Final Portfolio Project |
Career Paths
You’ll be prepared for roles in:
- Tech Companies
- Financial Institutions
- Healthcare Organizations
- Marketing Agencies
Salary Expectations
Nigeria: ₦2,500,000 – ₦5,000,000 per year
International: $70,000 – $100,000 per year
Features
- EXCEL
- POWER BI
- TABLEAU
- SQL
- PYTHON
- GITHUB
Target audiences
- Every individual with desire to learn a lubricative skill in the digital era
Requirements
- Make first part or full Payment (our payment structure permit 3 installment plan. Which are 3 months payment processing,)
- Access to computer/laptop
- Willingness to learn
