Data Science – Job-Oriented Professional Training Program

This Data Science – Job-Oriented Professional Training Program is designed to provide practical, industry-oriented training in Python programming, statistics, data analysis, data visualization, SQL, exploratory data analysis, machine learning, model evaluation, and introductory deep learning.

The course follows the complete Data Science lifecycle, starting from data collection and preparation through exploratory analysis, feature engineering, model development, evaluation, optimization, and business-oriented insights.

Students will work with industry-relevant technologies including Python, NumPy, Pandas, Jupyter Notebook, Matplotlib, Seaborn, SQL, Scikit-learn, TensorFlow/Keras, and visualization tools.

The program emphasizes hands-on coding, real-world datasets, practical assignments, machine-learning exercises, case studies, and an end-to-end Data Science project.

The syllabus is based on the structure of the RunnerDev Data Science program, which currently covers Python, statistics, data cleaning, EDA, SQL, machine learning, supervised and unsupervised algorithms, model optimization, and optional deep learning. 

By completing this program, learners will be able to:

• ☑ Understand the Data Science lifecycle
• ☑ Program using Python
• ☑ Work with NumPy and Pandas
• ☑ Use Jupyter Notebook
• ☑ Perform statistical analysis
• ☑ Collect and clean data
• ☑ Handle missing values and outliers
• ☑ Perform feature engineering
• ☑ Perform exploratory data analysis
• ☑ Create professional data visualizations
• ☑ Write SQL queries
• ☑ Connect Python with databases
• ☑ Build Machine Learning models
• ☑ Apply supervised learning algorithms
• ☑ Apply unsupervised learning algorithms
• ☑ Evaluate model performance
• ☑ Optimize ML models
• ☑ Perform hyperparameter tuning
• ☑ Apply cross-validation
• ☑ Understand overfitting and underfitting
• ☑ Understand introductory Deep Learning
• ☑ Work with TensorFlow/Keras
• ☑ Complete an end-to-end Data Science project
• ☑ Present data-driven business insights

Basic Computer Knowledge

Students should have:

• ☑ Basic computer knowledge
• ☑ Understanding of files and folders
• ☑ Basic internet and browser usage
• ☑ Basic command-line knowledge

Programming Knowledge

Basic programming knowledge is recommended but not mandatory.

Students should understand:

• ☑ Variables
• ☑ Data types
• ☑ Conditions
• ☑ Loops
• ☑ Functions
• ☑ Basic programming logic

Students without Python experience can learn Python fundamentals during the course.

Mathematics

Basic mathematics is sufficient.

Recommended knowledge:

• ☑ Percentages
• ☑ Averages
• ☑ Basic algebra
• ☑ Basic probability
• ☑ Basic statistics

Advanced mathematics is not required to start the course.

Recommended

• ☑ Basic SQL knowledge
• ☑ Basic database concepts
• ☑ Basic programming experience
• ☑ Basic Excel knowledge

Important

No prior professional Data Science or Machine Learning experience is required.

Operating System

Any one of the following:

• ☑ Windows 10/11
• ☑ Ubuntu Linux
• ☑ macOS

Windows users can optionally use WSL2 with Ubuntu for Linux-based exercises.

Python Environment

Students should install:

• ☑ Python
• ☑ pip
• ☑ Python virtual environment (venv)
• ☑ Jupyter Notebook
• ☑ JupyterLab

Python is the primary programming language used throughout the course.

Python Libraries

The following libraries should be installed:

Data Processing

• ☑ NumPy
• ☑ Pandas

Visualization

• ☑ Matplotlib
• ☑ Seaborn
• ☑ Plotly – optional

Machine Learning

• ☑ Scikit-learn

Statistics

• ☑ SciPy
• ☑ Statsmodels – recommended

Deep Learning – Optional

• ☑ TensorFlow
• ☑ Keras

Development Tools

Students can use either:

• ☑ Visual Studio Code
• ☑ PyCharm
• ☑ JupyterLab
• ☑ Jupyter Notebook

Python, JupyterLab, VS Code/PyCharm, Pandas, and other Python data-science tools as part of the broader data-science ecosystem.

Database & SQL Tools

Students should install at least one database:

• ☑ MySQL
• ☑ PostgreSQL
• ☑ SQLite

Database clients:

• ☑ DBeaver
• ☑ MySQL Workbench – optional
• ☑ pgAdmin – optional

Version Control

• ☑ Git
• ☑ GitHub account
• ☑ GitHub Desktop – optional

Recommended Hardware

For normal Data Science and Machine Learning exercises:

• ☑ 8 GB RAM minimum
• ☑ 16 GB RAM recommended
• ☑ 4-core processor or better
• ☑ 20–30 GB available disk space
• ☑ Stable internet connection

For larger datasets or deep-learning workloads, cloud notebooks or GPU-enabled environments may be used.

Module 1: Introduction to Data Science

✅ Overview of Data Science

✅ Data Science lifecycle

✅ Industry use cases

✅ Career opportunities & roles

✅ Tools used in Data Science

✅ Python fundamentals

✅ Data types, loops, and functions

✅ NumPy & Pandas

✅ Data handling & file operations

✅ Jupyter Notebook

✅ Descriptive statistics

✅ Probability & distributions

✅ Correlation & covariance

✅ Hypothesis testing

✅ Basics of linear algebra

✅ Data sources (CSV, Excel, APIs, Databases)

✅ Handling missing values

✅ Outlier detection

✅ Data transformation

✅ Feature engineering

✅ Data visualization techniques

✅ Matplotlib & Seaborn

✅ Pattern identification

✅ Insight generation

✅ Data storytelling

✅ Database fundamentals

✅ SQL queries (CRUD operations)

✅ Joins & subqueries

✅ Connecting SQL with Python

✅ Machine Learning concepts

✅ Supervised vs Unsupervised learning

✅ Model training & testing

✅ Evaluation metrics

✅ Linear Regression

✅ Logistic Regression

✅ Decision Trees

✅ Random Forest

✅ K-Nearest Neighbors (KNN)

✅ K-Means clustering

✅ Hierarchical clustering

✅ Principal Component Analysis (PCA)

✅ Feature selection

✅ Hyperparameter tuning

✅ Cross-validation

✅ Overfitting & underfitting

✅ Neural network basics

✅ TensorFlow / Keras

✅ Simple deep learning models

Students will create:

• ☑ Project source code
• ☑ Jupyter Notebook
• ☑ Cleaned dataset
• ☑ EDA report
• ☑ Data visualizations
• ☑ Feature-engineering pipeline
• ☑ Machine-learning models
• ☑ Model evaluation report
• ☑ Model comparison
• ☑ Business insights
• ☑ Project documentation
• ☑ Final presentation 

After completing this course, learners can target roles such as:

• ☑ Data Scientist
• ☑ Junior Data Scientist
• ☑ Associate Data Scientist
• ☑ Data Science Associate
• ☑ Machine Learning Engineer – Junior Level
• ☑ Junior Machine Learning Engineer
• ☑ Data Analyst
• ☑ Junior Data Analyst
• ☑ Business Data Analyst
• ☑ Data Analytics Associate
• ☑ Python Data Analyst
• ☑ Machine Learning Analyst
• ☑ Predictive Analytics Associate
• ☑ AI/ML Associate
• ☑ Data Science Consultant – Junior Level
• ☑ Research / Data Science Associate
• ☑ Business Intelligence / Analytics Associate

Note: Job titles and responsibilities vary by organization. This program is primarily designed to build a strong foundation for Data Science, Data Analytics, and entry-level Machine Learning roles.

Programming

• ☑ Python

Data Processing

• ☑ NumPy
• ☑ Pandas

Statistics

• ☑ SciPy
• ☑ Statsmodels

Visualization

• ☑ Matplotlib
• ☑ Seaborn
• ☑ Plotly – optional

Databases

• ☑ MySQL
• ☑ PostgreSQL
• ☑ SQLite

SQL

• ☑ SQL
• ☑ Advanced SQL
• ☑ Window Functions
• ☑ CTEs

Machine Learning

• ☑ Scikit-learn

Deep Learning – Optional

• ☑ TensorFlow
• ☑ Keras

Development Environment

• ☑ Jupyter Notebook
• ☑ JupyterLab
• ☑ Visual Studio Code
• ☑ PyCharm

Version Control

• ☑ Git
• ☑ GitHub 

After completing this program, learners should be able to take a dataset from raw data to meaningful business insights and predictive models.

The learner's practical journey will follow:

Python → NumPy → Pandas → Statistics → Data Cleaning → EDA → SQL → Feature Engineering → Machine Learning → Model Evaluation → Optimization → Deep Learning → Real-World Project

Final Career Skill Path

Python Developer Fundamentals → Data Analyst → Data Science → Machine Learning → Advanced Data Science