Setting your foot strongly in the field of Data Science

Professional course in Data Analysis

Course duration: 3 months

Course details:

Month 1 (Introduction to Data Analysis)

Week 1: Getting Started with Data Analysis

• Introduction to data analysis and its importance
• Understanding the data analysis process
• Overview of tools and programming languages for data analysis

Weekly Hands-on practical session Week 2: Exploring Data with Descriptive Statistics

• Types of data: categorical and numerical
• Measures of central tendency: mean, median, mode
• Measures of dispersion: range, variance, standard deviation
• Interpreting data distributions and histograms

Weekly Hands-on practical session Week 3: Data Visualization with Matplotlib

• Introduction to data visualization
• Creating line plots, bar charts, and scatter plots
• Customizing plots: labels, titles, legends
• Visualizing relationships and patterns in data

Weekly Hands-on practical session Week 4: Data Cleaning and Preprocessing

• Identifying and handling missing data
• Dealing with duplicate and inconsistent data
• Data transformation: scaling, normalization, encoding
• Feature engineering for better analysis

Weekly Hands-on practical session

"Tracking technology is a powerful tool that can be used for good or for evil. It's important to be aware of how our data is being tracked and to use this technology responsibly."

-Tim Cook, CEO of Apple

Month 2 (Statistical Analysis and Hypothesis Testing)

Week 5: Introduction to Probability and Distributions

• Basic concepts of probability
• Common probability distributions: normal, binomial
• Using probability distributions for analysis

Weekly Hands-on practical session Week 6: Correlation and Regression Analysis

• Understanding correlation and its types
• Simple linear regression: fitting and interpreting the model
• Evaluating regression models: R-squared, residuals

Weekly Hands-on practical session Week 7: Introduction to Hypothesis Testing

• Formulating null and alternative hypotheses
• Types of hypothesis tests: t-test, chi-squared test
• Interpreting p-values and making decisions

Weekly Hands-on practical session Week 8: A/B Testing and Confidence Intervals

• Designing and conducting A/B tests
• Calculating confidence intervals
• Applying statistical tests to real-world scenarios

Weekly Hands-on practical session

Month 3 (Data Analysis Projects and Advanced Topics)

Week 9: Exploratory Data Analysis (EDA)

• Using EDA techniques to understand data patterns
• Identifying outliers and anomalies
• Storytelling with data: creating meaningful narratives

Weekly Hands-on practical session Week 10: Time Series Analysis

• Analyzing time-dependent data
• Creating and interpreting time series plots
• Forecasting using methods like moving averages

Weekly Hands-on practical session Week 11: Advanced Visualization with Seaborn

• Creating more complex visualizations using Seaborn
• Heatmaps, pair plots, violin plots, and more
• Visualizing multi-dimensional data relationships

Weekly Hands-on practical session Week 12: Final Data Analysis Project and Review

• Working on a comprehensive data analysis project
• Reviewing key concepts and techniques
• Advice for further learning and specialization

Weekly Hands-on practical session on IMEI Tracking

"Tracking technology is a double-edged sword. It can be used to track our movements and activities, but it can also be used to help us stay safe and healthy. It's important to use this technology wisely."

-Shoshana Zuboff, author of "The Age of Surveillance Capitalism"

Bonus: Free Data Analysis tools and Resources

👉 Cost of programme: $120/N100k