1.1 Understanding the Role of a Data Analyst

  – What is data analysis? Overview of the data analysis process.

  – Key responsibilities of a Data Analyst.

  – Common data analyst tools and technologies.

1.2 Types of Data

  – Structured vs. Unstructured data.

  – Data types: Numerical, categorical, ordinal.

  – Introduction to databases, data warehouses, and big data.

1.3 Data Life Cycle

  – Understanding the data lifecycle: collection, cleaning, analysis, interpretation, and reporting.

  – The importance of data-driven decision-making.

2.1 Data Collection Techniques

  – Methods of data collection: surveys, APIs, scraping, and database queries.

  – Tools for data collection (e.g., Google Sheets, SQL, Python libraries).

2.2 Introduction to Databases

  – Understanding databases and SQL.

  – Overview of relational databases (MySQL, PostgreSQL, etc.).

  – Writing basic SQL queries for data extraction.

2.3 Case Study: Data Extraction

  – Extracting data from a relational database using SQL.

  – Applying data extraction techniques to a real-world scenario.

3.1 Data Cleaning Techniques

  – Handling missing data: Imputation, removing null values.

  – Dealing with outliers and duplicates.

  – Formatting data correctly for analysis (data types, consistency).

3.2 Data Preparation for Analysis

  – Data normalization and scaling.

  – Combining and merging datasets (joins, concatenation).

  – Feature engineering: Creating new variables for analysis.

3.3 Tools for Data Cleaning

  – Introduction to Microsoft Excel for data cleaning.

  – Using Python (pandas library) for data manipulation.

  – Cleaning data in SQL using built-in functions.

3.4 Case Study: Data Cleaning Project

  – Cleaning a messy dataset in Excel and Python.

  – Ensuring data is prepared for analysis.

4.1 Understanding EDA

  – The purpose of exploratory data analysis.

  – Identifying trends, patterns, and anomalies in datasets.

  – Generating insights through EDA.

4.2 Descriptive Statistics

  – Central tendency measures: Mean, median, mode.

  – Dispersion measures: Variance, standard deviation, range.

  – Distribution of data: Normal, skewness, kurtosis.

4.3 Data Visualization Techniques

  – Creating visualizations to explore data.

  – Tools for visualization: Excel, Python (matplotlib, seaborn).

  – Visualizing distributions, correlations, and trends.

4.4 Case Study: Conducting EDA

  – Performing EDA on a business dataset using Python.

  – Drawing insights and creating a summary report.

5.1 Importance of Data Visualization

  – Understanding the role of data visualization in decision-making.

  – Best practices for effective visual communication.

5.2 Data Visualization Tools

  – Introduction to data visualization tools: Tableau, Power BI, Python (matplotlib, seaborn), and Excel.

  – Creating basic charts: bar charts, line charts, scatter plots, histograms.

5.3 Advanced Visualization Techniques

  – Creating dashboards and interactive reports in Tableau/Power BI.

  – Advanced charts: Heatmaps, boxplots, waterfall charts, and geospatial maps.

5.4 Case Study: Creating Interactive Dashboards

  – Designing a dashboard in Tableau/Power BI for business insights.

  – Presenting a story through data visualization.

6.1 Introduction to Statistical Analysis

  – Understanding the importance of statistics in data analysis.

  – Basic probability concepts: Probability distributions, expected value, variance.

6.2 Hypothesis Testing

  – Introduction to null and alternative hypotheses.

  – Performing t-tests, chi-square tests, and ANOVA.

  – Understanding p-values and statistical significance.

6.3 Correlation and Regression Analysis

  – Understanding correlation and causation.

  – Simple and multiple linear regression.

  – Interpreting regression results for business insights.

6.4 Case Study: Statistical Analysis for Decision Making

  – Conducting hypothesis testing and regression analysis on a dataset.

  – Providing actionable insights based on statistical analysis.

7.1 What is Business Intelligence (BI)?

  – Understanding the role of BI in business decision-making.

  – How data analysts contribute to BI processes.

7.2 BI Tools Overview

  – Overview of popular BI tools: Tableau, Power BI, QlikView.

  – Differences between traditional reporting and BI dashboards.

7.3 Using Power BI and Tableau

  – Connecting data sources and building reports in Power BI/Tableau.

  – Creating automated and dynamic dashboards.

  – Sharing insights with stakeholders.

7.4 Case Study: Implementing BI Solutions

  – Using Power BI or Tableau to solve a business problem.

  – Designing a dynamic dashboard for management reporting.

8.1 Introduction to Big Data

  – Understanding big data concepts: Volume, variety, velocity, and veracity.

  – The importance of big data in modern analytics.

8.2 Introduction to Hadoop and Spark

  – Overview of big data technologies: Hadoop, Spark, and NoSQL databases.

  – Basics of working with big data in Python using PySpark.

8.3 Case Study: Analyzing Big Data

  – Working on a big data problem using Spark or another big data tool.

  – Processing large datasets and deriving insights.

9.1 Capstone Project: End-to-End Data Analysis

  – Defining a business problem and gathering relevant data.

  – Performing data cleaning, EDA, and statistical analysis.

  – Visualizing data insights and presenting them in a dashboard or report.

9.2 Presentation and Feedback

  – Presenting the capstone project findings to peers and instructors.

  – Receiving feedback and suggestions for improvement.