Master Data Science: Python, Machine Learning, AI +More
Ready to dive into the world of data and turn raw information into powerful insights that drive innovation and business success? At Nepthink Solutions, our hands-on Data Science Training equips you with the tools, techniques, and real-world experience you need to analyze data, build predictive models, and become a data-driven problem solver using Python, SQL, and essential libraries like Pandas and Scikit-learn.
Data Science is one of the fastest-growing and highest-paying fields in tech today. From healthcare to finance to tech startups, companies are looking for professionals who can make sense of complex data and guide strategic decisions. This course sets you on the path to becoming a valuable asset in any data-driven organization.
Our Port Harcourt-based Data Science course is perfect for absolute beginners, developers, or analysts looking to expand their skill set. With a project-based curriculum, you’ll build a strong portfolio as you work on real-world datasets and challenges that reflect current industry demands.
You’ll be mentored by experienced data professionals who will guide you through core topics like data wrangling, data visualization, machine learning, statistics, and AI fundamentals. You’ll also explore popular tools such as Jupyter Notebooks, Tableau, NumPy, and TensorFlow—all while developing a deep understanding of how to solve real business problems with data.
Join the Best Data Science Academy in Port Harcourt Today!
Looking for the best Data Science school or affordable Data Science training in Port Harcourt? Nepthink Technologies offers cutting-edge training that prepares you for high-paying roles in tech and beyond. Build the future with data—start your journey with us.
What You Will Learn
Section 1: Introduction to Data Science
- What is Data Science? Tools and Applications
- Overview of the Data Science Lifecycle
- Setting Up Your Data Science Environment
- Introduction to Python for Data Science
Section 2: Data Wrangling & Preprocessing
- Working with Pandas and Numpy
- Handling Missing Data
- Data Cleaning Techniques
- Data Transformation & Feature Engineering
Section 3: Data Visualization
- Exploratory Data Analysis (EDA)
- Creating Plots with Matplotlib and Seaborn
- Dashboards with Plotly and Streamlit
- Storytelling with Data
Section 4: Statistics for Data Science
- Descriptive and Inferential Statistics
- Probability Distributions and Sampling
- Hypothesis Testing
- Correlation vs Causation
Section 5: Machine Learning Fundamentals
- Supervised vs Unsupervised Learning
- Regression and Classification Models
- Model Evaluation Metrics
- Train-Test Split and Cross-Validation
Section 6: Advanced Machine Learning
- Ensemble Methods: Random Forest, XGBoost
- Clustering Techniques: K-Means, DBSCAN
- Dimensionality Reduction: PCA, t-SNE
- Model Tuning & Hyperparameter Optimization
Section 7: Databases & SQL for Data Science
- Introduction to Relational Databases
- SQL Queries: SELECT, JOIN, GROUP BY
- Using SQL with Pandas
- Data Extraction from APIs
Section 8: Big Data & Cloud Tools
- Working with Big Data Concepts
- Introduction to Spark and Hadoop
- Using Google Colab and Jupyter
- Cloud Storage & Data Pipelines Basics
Section 9: Real-World Projects
- Building a Predictive Analytics Model
- Customer Segmentation Project
- Time Series Forecasting
- Capstone Project Walkthrough
Section 10: Ethics, Careers & Portfolio
- Ethics in Data Science & Responsible AI
- How to Build a Data Science Portfolio
- Resume & LinkedIn Optimization
- Freelancing and Getting Hired
Section 11: Graduation & Certification
- Final Assessment & Interview Prep
- Issuance of Certificate
Course Content:
- What is Data Science?
- Applications of Data Science
- Roles in a Data Science Team
- Overview of the Data Science Lifecycle
- Setting Up the Development Environment
- Python Basics and Syntax
- Variables, Data Types, and Operators
- Control Structures and Functions
- Working with Libraries: NumPy, Pandas
- Jupyter Notebook Essentials
- Loading Data from Various Sources
- Handling Missing Values
- Dealing with Duplicates and Outliers
- Data Transformation Techniques
- Feature Engineering Basics
- Understanding Data Distributions
- Creating Charts with Matplotlib
- Advanced Visualizations with Seaborn
- Interactive Dashboards with Plotly
- Storytelling with Data
- Descriptive Statistics
- Probability Theory Basics
- Inferential Statistics
- Hypothesis Testing
- Correlation vs Causation
- What is Machine Learning?
- Supervised vs Unsupervised Learning
- Regression and Classification
- Training and Evaluating Models
- Model Selection and Overfitting
- Ensemble Methods: Bagging and Boosting
- Random Forest and XGBoost
- Clustering Techniques: K-Means, DBSCAN
- Dimensionality Reduction: PCA
- Hyperparameter Tuning
- Introduction to Relational Databases
- Basic SQL Queries: SELECT, WHERE, JOIN
- Aggregate Functions and Subqueries
- Integrating SQL with Python
- Data Extraction and API Usage
- Introduction to Big Data Concepts
- Apache Spark Basics
- Cloud Platforms Overview (AWS, GCP)
- Using Google Colab and Notebooks
- Deploying Models on the Cloud
- End-to-End Data Science Project
- Predictive Modeling
- Data-Driven Dashboards
- Capstone Project
- Building Your Portfolio
- Preparing for Interviews
- Resume & LinkedIn Optimization
- Freelancing as a Data Scientist
- Mock Interviews and Review
- Certificate Issuance
