Data Analytics

Course Overview:

This course provides a comprehensive, hands-on introduction to the complete data analytics pipeline from data collection and cleaning to exploratory analysis, visualization, and reporting. Students will gain practical, real-world experience using industry-standard tools such as Excel, Python (Pandas & NumPy), SQL, and Power BI, and IBM SPSS Statistics to analyze real-world datasets across multiple domains.

The course places strong emphasis on analytical thinking, problem-solving, and the ability to translate raw data into actionable business insights that support data-driven decision-making.

In addition, the course includes an introductory module on Machine Learning for Analysts, focusing on essential supervised learning techniques such as linear and logistic regression, model evaluation, and train/test workflows.

Learning Outcomes:

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

• Understand the complete data lifecycle and clearly articulate the role and responsibilities of a data analyst.
• Identify and describe different types of data structures, file formats, and data sources used in analytics projects.
• Explain and apply the key steps of the data analysis process, including data collection, wrangling, mining, visualization, and interpretation.
• Develop foundational Python programming skills, including basic syntax, data types, variables, functions, and control flow.
• Use Python libraries such as Pandas and NumPy to preprocess, analyze, and manipulate datasets.
• Analyze data stored in relational databases using SQL queries, joins, aggregations, and filtering techniques.
• Design and create relational databases using SQL Data Definition Language (DDL) to manage tables, structures, and relationships.
• Create effective visualizations and dashboards to communicate insights to stakeholders using tools such as Power BI.
• Apply introductory machine learning concepts—such as train/test splits, supervised learning, and model evaluation metrics—to solve basic predictive analytics problems.
• Perform advanced statistical analysis using IBM SPSS Statistics, including data management, descriptive analysis, visualization, and predictive modeling.

Prerequisites

• Basic knowledge Python & SQL

Target Certification:

Participants who pass the course exam will obtain:

• A Certificate from MIU.
• An IBM Data Analyst Professional Certificate.

Who Should Attend:

• Students or graduates are interested in Data Analytics.
• Beginners who want to build practical data analysis skills.
• Learners are interested in using tools such as Excel, Python, SQL, Power BI, and SPSS.

Contents:

• Introduction to Data Analysis
  • Data lifecycle
    • Data collection
    • Data cleaning
    • Data analysis
    • Data visualization
    • Reporting & decision making
  • Types of data
    • Structured vs unstructured
    • Numerical vs categorical
  • Basic statistics
    • Mean, median, mode
    • Variance, standard deviation
    • Normal distribution
    • Outliers
• Python for Data Analysis
  • Python basics
    • Variables
    • Data types
    • Functions
    • Loops & conditionals
• Libraries
  • Pandas
    • DataFrames
    • Importing CSV/Excel/SQL
    • Filtering, sorting
    • GroupBy
    • Aggregations
    • Merging & joining datasets
    • Pivot tables
    • Handling missing values
  • NumPy
    • Arrays
    • Mathematical operations
    • Broadcasting
• SQL for Data Retrieval & Analysis
  • SQL Basics (SELECT, WHERE, ORDER BY)
  • Aggregations (GROUP BY, HAVING)
  • Joins (INNER, LEFT, RIGHT, FULL)
  • Subqueries & Common Table Expressions (CTEs)
  • Window Functions (RANK, ROW_NUMBER, LAG/LEAD)
  • SQL Data Cleaning
  • Query Optimization Concepts (indexes, EXPLAIN)
• Data Cleaning & Preprocessing
  • Dealing with Missing Values
  • Detecting & Treating Outliers
  • Date and Time Formatting
  • String/Text Cleaning
  • Encoding Categorical Variables
  • Standardization vs Normalization
• Data Visualization & Dash-boarding
  • Best Practices in Visualization
  • Chart Types & When to Use Them
  • Designing Effective Dashboards
• Introduction to Machine Learning for Analysts
  • Supervised Learning Overview
  • Linear Regression
  • Logistic Regression
  • Train/Test Splits
  • Model Evaluation Metrics
• Advanced Statistical Analysis Using IBM SPSS Statistics
  • Overview of SPSS environment and interface
  • Data Editor vs. Variable View
  • Data Management & Preparation
  • visualization tools: histograms, bar charts, boxplots, scatterplots
  • Modeling & Predictive Analytics

Recommended Next Course:

• Machine Learning Fundamentals
• Business Intelligence with Power BI
• Advanced Data Analytics

JOB Profile:

• Data Analyst
• Business Intelligence Analyst
• Junior Data Scientist
• Reporting Analyst
• Data Visualization Specialist

Work Environments:

• Banks, healthcare, telecom, and retail sectors
• Data analytics departments
• Business intelligence teams
• IT and software companies
• Research centers and consulting companies

Estimated Time to Completion: 58 hours

Time Plan: Batch 1_July 2026

Day	Topics & Activities	No. Of Hours	Date	Time	Location
Day 1	What is Data Analysis? Data lifecycle Types of data File formats: CSV, Excel, SQL, JSON Basic statistics: mean, median, mode, variance, standard deviation Identifying outliers	6 H	July 5, 2026	From 9 AM to 3 PM	on campus
Day 2	SQL for Data Retrieval & Analysis Core Concepts of Relational Databases SQL Command Categories SQL Basics (SELECT, WHERE, ORDER BY) Aggregations (GROUP BY, HAVING)	6 H	July 6, 2026	From 9 AM to 3 PM	on campus
Day 3	Python Foundations Variables, data types Lists, dictionaries Loops & conditionals Functions String operations Python coding exercises	5 H	July 7, 2026	From 6 PM to 11 PM	online
Day 4	Python Libraires Pandas and NumPy DataFrames Importing CSV/Excel/SQL Filtering, sorting GroupBy Aggregations Merging & joining datasets Pivot tables Handling missing values	5 H	July 8, 2026	From 6 PM to 11 PM	online
Day 5	Data Cleaning & Preprocessing Dealing with Missing Values Detecting & Treating Outliers Date and Time Formatting String/Text Cleaning Encoding Categorical Variables Standardization vs Normalization	6 H	July 12, 2026	From 9 AM to 3 PM	on campus
Day 6	Data Visualization & Dash-boarding Best Practices in Visualization Chart Types & When to Use Them Data Visualization with Python	5 H	July 13, 2026	From 6 PM to 11 PM	online
Day 7 + Day 8	Introduction to Machine Learning for Analysts Supervised Learning Overview Linear Regression Logistic Regression Train/Test Splits Model Evaluation Metrics	10 H	July 14, 2026 + July 15, 2026	From 6 PM to 11 PM	online
Day 9	Statistical Analysis Using IBM SPSS Statistics Overview of SPSS environment and interface Data Editor vs. Variable View Data Management & Preparation visualization tools: histograms, bar charts, boxplots, scatterplots Modeling & Predictive Analytics	5 H	July 19, 2026	From 9 AM to 2 PM	on campus
Day 10	Case study and mini project	5 H	July 20, 2026	From 6 PM to 11 PM	online
Day 11	Case study and mini project (Cont.)	5 H	July 21, 2026	From 6 PM to 11 PM	online

Time Plan: Batch 2_August 2026

Day	Topics & Activities	No. Of Hours	Date	Time	Location
Day 1	What is Data Analysis? Data lifecycle Types of data File formats: CSV, Excel, SQL, JSON Basic statistics: mean, median, mode, variance, standard deviation Identifying outliers	6 H	August 16, 2026	From 9 AM to 3 PM	on campus
Day 2	SQL for Data Retrieval & Analysis Core Concepts of Relational Databases SQL Command Categories SQL Basics (SELECT, WHERE, ORDER BY) Aggregations (GROUP BY, HAVING)	6 H	August 17, 2026	From 9 AM to 3 PM	on campus
Day 3	Python Foundations Variables, data types Lists, dictionaries Loops & conditionals Functions String operations Python coding exercises	5 H	August 18, 2026	From 6 PM to 11 PM	online
Day 4	Python Libraires Pandas and NumPy DataFrames Importing CSV/Excel/SQL Filtering, sorting GroupBy Aggregations Merging & joining datasets Pivot tables Handling missing values	5 H	August 19, 2026	From 6 PM to 11 PM	online
Day 5	Data Cleaning & Preprocessing Dealing with Missing Values Detecting & Treating Outliers Date and Time Formatting String/Text Cleaning Encoding Categorical Variables Standardization vs Normalization	6 H	August 23, 2026	From 9 AM to 3 PM	on campus
Day 6	Data Visualization & Dash-boarding Best Practices in Visualization Chart Types & When to Use Them Data Visualization with Python	5 H	August 24, 2026	From 6 PM to 11 PM	online
Day 7 + Day 8	Introduction to Machine Learning for Analysts Supervised Learning Overview Linear Regression Logistic Regression Train/Test Splits Model Evaluation Metrics	10 H	August 25, 2026 + August 26, 2026	From 6 PM to 11 PM	online
Day 9	Statistical Analysis Using IBM SPSS Statistics Overview of SPSS environment and interface Data Editor vs. Variable View Data Management & Preparation visualization tools: histograms, bar charts, boxplots, scatterplots Modeling & Predictive Analytics	5 H	August 30, 2026	From 9 AM to 2 PM	on campus
Day 10	Case study and mini project	5 H	August 31, 2026	From 6 PM to 11 PM	online
Day 11	Case study and mini project (Cont.)	5 H	September 1, 2026	From 6 PM to 11 PM	online