Data Science & Analytics with Python Training Course

Introduction

In today's data-driven world, the ability to extract meaningful insights from vast datasets is a critical skill. This Data Science & Analytics with Python Training Course is designed to equip aspiring data scientists, data analysts, business analysts, statisticians, and researchers with the comprehensive Python skills needed for end-to-end data analysis. Leveraging Python's powerful ecosystem, this program covers everything from data collection and cleaning to visualization, statistical modeling, and basic machine learning.

Participants will gain hands-on experience with industry-standard Python libraries such as Pandas for data manipulation, NumPy for numerical operations, Matplotlib and Seaborn for data visualization, and SciPy/StatsModels for statistical analysis. The curriculum also introduces fundamental machine learning concepts to enable predictive modeling. By the end of this course, you will be proficient in performing exploratory data analysis (EDA), building predictive models, and effectively communicating complex data insights, empowering you to make data-driven decisions and solve real-world problems.

Target Audience

• Aspiring Data Scientists and Data Analysts.
• Business Analysts seeking to enhance their analytical capabilities.
• Statisticians and Researchers looking to apply Python to their work.
• Software Developers interested in transitioning to data roles.
• Anyone interested in developing strong data analysis and interpretation skills.

Duration

10 days

Course Objectives

1. Understand the core concepts of data science and the end-to-end data analysis workflow.
2. Master Python fundamentals essential for data manipulation and analysis.
3. Gain proficiency in using the Pandas library for efficient data handling and transformation.
4. Develop skills in data cleaning, preprocessing, and handling various data imperfections.
5. Perform comprehensive Exploratory Data Analysis (EDA) to uncover patterns and insights.
6. Create compelling and informative data visualizations using Matplotlib and Seaborn.
7. Apply statistical methods and hypothesis testing using Python libraries.
8. Build and evaluate basic machine learning models for predictive analytics and effectively communicate data-driven insights.

Course Content

Module 1. Introduction to Data Science & Analytics

• What is Data Science and its relationship with Data Analytics, ML, AI
• The Data Science lifecycle: Problem understanding, data acquisition, cleaning, exploration, modeling, deployment, communication
• Types of data: Structured, semi-structured, unstructured
• Roles in the data ecosystem: Data Analyst, Data Scientist, ML Engineer, Data Engineer
• Setting up your Python environment: Anaconda, Jupyter Notebook/Lab

Module 2. Python for Data Science Fundamentals

• Python basics: Variables, data types (integers, floats, strings, booleans)
• Python data structures: Lists, Tuples, Dictionaries, Sets
• Control flow: If/Else statements, For loops, While loops
• Functions: Defining and calling functions, lambda functions
• Introduction to NumPy: N-dimensional arrays for numerical operations

Module 3. Data Manipulation with Pandas

• Introduction to Pandas: Series and DataFrames
• Creating DataFrames from various sources (dictionaries, lists, CSV, Excel)
• Indexing and selecting data: loc, iloc, boolean indexing
• Data alignment and arithmetic operations
• Grouping and aggregation with groupby()
• Merging, joining, and concatenating DataFrames

Module 4. Data Cleaning & Preprocessing

• Identifying and handling missing values (NaN): Imputation, dropping rows/columns
• Handling duplicate data: Detection and removal
• Data type conversion and consistency
• Data formatting and standardization
• Outlier detection and treatment strategies
• Feature engineering basics: Creating new variables from existing ones

Module 5. Exploratory Data Analysis (EDA)

• Descriptive statistics: Mean, median, mode, variance, standard deviation, quartiles
• Understanding data distributions: Histograms, box plots, density plots
• Correlation analysis: Pearson, Spearman correlation coefficients
• Covariance and scatter plots for relationship visualization
• Practical application of EDA techniques to real-world datasets

Module 6. Data Visualization with Matplotlib & Seaborn

• Introduction to Matplotlib: Creating basic plots (line, bar, scatter, histogram)
• Customizing plots: Titles, labels, legends, colors, styles
• Subplots and multiple plots
• Introduction to Seaborn: Enhancing statistical plots
• Advanced visualizations: Heatmaps, pair plots, violin plots, joint plots
• Principles of effective data visualization and storytelling

Module 7. Statistical Analysis with SciPy & StatsModels

• Probability distributions: Normal, Binomial, Poisson
• Hypothesis testing fundamentals: Null and alternative hypotheses, p-value, significance level
• T-tests (one-sample, independent, paired), ANOVA
• Linear Regression: Simple and Multiple Linear Regression using StatsModels and SciPy
• Introduction to other statistical models (e.g., Logistic Regression concepts)
• Confidence intervals and statistical inference

Module 8. Introduction to Machine Learning for Data Analysis

• Overview of Machine Learning: Supervised vs. Unsupervised Learning
• Regression tasks: Predicting continuous values (e.g., predicting house prices)
• Classification tasks: Predicting categorical labels (e.g., classifying emails as spam)
• Basic ML algorithms: Linear Regression, Logistic Regression, Decision Trees
• Model evaluation metrics: R-squared, MSE, Accuracy, Precision, Recall, F1-score
• Introduction to Scikit-learn: Basic model training and prediction

Module 9. Time Series Analysis Basics

• Understanding time series data: Components (trend, seasonality, noise)
• Resampling and aggregation of time series data
• Simple forecasting methods: Moving averages, Exponential Smoothing
• Introduction to ARIMA models (Autoregressive Integrated Moving Average)
• Using Prophet for time series forecasting (if time permits)

Module 10. Working with Different Data Sources

• Reading and writing various data formats: CSV, Excel, JSON, XML
• Interacting with SQL databases using Python (e.g., SQLite, PostgreSQL connector)
• Basic web scraping for data collection (e.g., using BeautifulSoup, Requests)
• Consuming data from APIs (Application Programming Interfaces)
• Data ingestion and pipeline considerations

Module 11. Communicating Data Insights

• The art of storytelling with data: Structure, narrative, visuals
• Designing effective dashboards and reports
• Presenting insights to non-technical stakeholders
• Tools for creating interactive dashboards (e.g., Dash, Streamlit - overview)
• Best practices for data presentation and ethical communication

Module 12. Capstone Project & Best Practices

• End-to-end data analysis project: From problem definition to insightful report
• Project planning and execution
• Code organization and reproducibility in data science projects
• Version control with Git/GitHub for data projects
• Review of best practices for data science workflow and continuous learning

General remarks

• Customizable courses are available to address the specific needs of your organization.
• The participant must be conversant in English
• Participants who successfully complete this course will receive a certificate of completion from Lenol Development Center.
• The course fee for onsite training includes facilitation training materials, tea break and lunch.
• Accommodation and airport pick up are made upon request
• For any inquiries reach us through info@lenoldevelopmentcenter.com or +254 710 314 746
• Payment should be made to our bank