L'EARN ICT Academy - The 2025 Professional Data Analysis Course

Overview

Course Description

Course Overview

This 12-week intensive course is designed to transform students into proficient data analysts. The course covers the principles of data analysis, statistical methods, data visualization, and machine learning.

Week-by-Week Breakdown

Week 1 - Introduction to Data Analysis: Understanding the role of a data analyst, introduction to data types, data structures, and data sources.
Week 2 - Data Cleaning & Preprocessing: Techniques for handling missing data, outliers, and data transformation.
Week 3 - Exploratory Data Analysis: Descriptive statistics, data distributions, correlation, and data visualization.
Week 4 - Inferential Statistics: Hypothesis testing, confidence intervals, t-tests, chi-square tests.
Week 5 - Regression Analysis: Simple and multiple linear regression, logistic regression, model evaluation.
Week 6 - Time Series Analysis: Understanding time series data, trend analysis, seasonality, ARIMA models.
Week 7 - Machine Learning Basics: Introduction to machine learning, supervised and unsupervised learning, classification and clustering.
Week 8 - Advanced Machine Learning: Decision trees, random forests, SVM, k-means clustering, PCA.
Week 9 - Data Visualization Tools: Mastery of data visualization tools like Matplotlib, Seaborn, Plotly.
Week 10 - Big Data Analysis: Introduction to big data, Hadoop, Spark, and working with large datasets.
Week 11 - SQL for Data Analysis: Writing complex SQL queries, joining tables, aggregations, subqueries.
Week 12 - Capstone Project & Career Prep: Students will work on a capstone project analyzing a real-world dataset, and receive career preparation including resume reviews, mock interviews, and job search strategies.

Learning Outcomes

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

Understand the principles of data analysis and statistical methods.
Clean and preprocess data for analysis.
Conduct exploratory and inferential data analysis.
Apply regression analysis and time series analysis to interpret data.
Use machine learning algorithms to make predictions from data.
Visualize data using various data visualization tools.
Analyze big data using Hadoop and Spark.
Use SQL for complex data analysis tasks.
Complete a capstone project showcasing their data analysis skills.

DAY TO DAY SESSION AND CLASS SCHEDULES

Week 1: Introduction to Data Analysis
Class 1: Types of data, data collection methods, and data quality. Class 2: Basic statistics: Mean, median, mode, standard deviation, and variance. Class 3: Importance of data visualization, types of charts and graphs.

Week 2: Introduction to Data Analysis
Class 4: Understanding data types and structures. Class 5: Introduction to descriptive statistics. Class 6: Data visualization tools and techniques.

Week 3: Introduction to Python
Class 7: Python basics: Variables, data types, loops, and conditional statements. Class 8: Introduction to NumPy and pandas. Class 9: Basic data manipulation with pandas.

Week 4: Python for Data Analysis
Class 10: Advanced data manipulation with pandas. Class 11: Data visualization with Matplotlib. Class 12: Importing/exporting data and handling missing data.

Week 5: Introduction to SQL
Class 13: Database concepts and SQL syntax. Class 14: SELECT statements, filtering data with WHERE clause. Class 15: Sorting data and using basic SQL functions.

Week 6: SQL for Data Analysis
Class 16: INNER JOIN and other types of joins. Class 17: Aggregating data with GROUP BY and HAVING. Class 18: Advanced SQL queries and subqueries.

Week 7: Advanced Python and Django
Class 19: Advanced Python concepts: Functions and error handling. Class 20: Setting up a Django project and understanding models. Class 21: Django views and templates.

Week 8: Django for Data Analysis
Class 22: Connecting Django with databases. Class 23: Querying data in Django. Class 24: Displaying data on web pages using Django.

Week 9: Data Visualization with Tableau
Class 25: Introduction to Tableau and connecting to data sources. Class 26: Creating basic visualizations in Tableau. Class 27: Building interactive dashboards in Tableau.

Week 10: Data Visualization with Power BI
Class 28: Introduction to Power BI and connecting to data sources. Class 29: Creating reports and visualizations in Power BI. Class 30: Using DAX functions and Power Query for advanced analysis.

Week 11: Advanced Excel for Data Analysis
Class 31: Excel basics: Formulas and functions. Class 32: Data analysis tools: Pivot tables and pivot charts. Class 33: Advanced Excel functions: VLOOKUP, HLOOKUP, INDEX, MATCH, and conditional formatting.

Week 12: Final Project and Review
Class 34: Applying all learned skills to a real-world data analysis project. Class 35: Review of all topics covered and addressing any questions. Class 36: Discussing best practices and final thoughts.

This schedule ensures a gradual progression through the topics, allowing you to build a strong foundation in data analysis while gaining proficiency in Python, Django, SQL, Tableau, Power BI, and Advanced Excel. Enjoy your learning journey!

This course is designed to be hands-on, with numerous practical exercises and projects to ensure students gain practical experience. It is suitable for beginners with a basic understanding of mathematics and programming concepts, as well as experienced professionals looking to expand their skill set.