Learn Data Science Course | Get Professional Tech Skills & IT Certification at Elevate Tech School

9 modules

42 lessons to be learned to complete this course

5 months

Intensive Classroom + Lab + Online Learning + Quiz

Professional level

No prerequisite courses or skills required to get started

Professional Diploma

To be awarded as achievement after course completion

NGN 200,000.00

Flexible payment term. 5% discount on one-time full payment

Enroll now

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Course Description

Data Science course is a broad field that includes Data collection and cleaning, Exploratory analysis, Machine learning and predictive modeling, Data visualization, AI development and automation. It’s about discovering patterns, building models, and generating insights from large, complex, and often unstructured datasets.

Data science course teaches you to analyze data using mathematics, statistics, machine learning, and programming to extract valuable insights and make predictions.

What You Will Learn
A comprehensive data science program will cover:

Programming: Python or R for data manipulation and analysis.
Mathematics & Statistics: Foundations for data interpretation and analysis.
Machine Learning & Artificial Intelligence: Concepts and applications for predictive modeling.
Data Wrangling & Databases: Cleaning, transforming, and managing data using tools like SQL.
Data Visualization: Creating meaningful charts and dashboards with tools like Tableau or Power BI.
Big Data Concepts: Understanding and working with large datasets.
Data Ethics: Understanding the ethical implications of data use.

Course Curriculum

Python Programming for Data Science

Python syntax and data types

Control structures and functions

Lists, dictionaries, sets, tuples

File handling, modules, and error handling

Object-oriented programming (OOP)

Lab: Build a data-cleaning script using Python

Statistics & Probability

Descriptive statistics: mean, median, mode, variance

Probability distributions: normal, binomial, Poisson

Inferential statistics: hypothesis testing, confidence intervals

Bayesian statistics and modeling

Lab: Analyze survey data and test hypotheses

Data Wrangling & Preprocessing

Handling missing data and outliers

Data transformation and encoding

Feature scaling and normalization

Pandas and NumPy for data manipulation

Lab: Clean and prepare a real-world dataset

Data Visualization

Principles of effective visualization

Charts and graphs: bar, line, scatter, heatmaps

Tools: Matplotlib, Seaborn, Plotly

Lab: Create an interactive dashboard using Python

Machine Learning Fundamentals

Supervised learning: regression, classification

Unsupervised learning: clustering, dimensionality reduction

Model evaluation: accuracy, precision, recall, F1-score

Overfitting, cross-validation, and regularization

Lab: Build a predictive model for housing prices

Deep Learning & Neural Networks

Introduction to neural networks

Activation functions and backpropagation

Convolutional Neural Networks (CNNs)

Recurrent Neural Networks (RNNs)

Lab: Image classification using CNN

Natural Language Processing (NLP)

Text preprocessing: tokenization, stemming, lemmatization

Sentiment analysis and topic modeling

Word embeddings: TF-IDF, Word2Vec

Lab: Analyze tweets for sentiment using NLP

Big Data & Cloud Integration

Introduction to big data tools: Hadoop, Spark

Cloud platforms: AWS, Azure, GCP

Working with APIs and web scraping

Lab: Collect and analyze data from a public API

Capstone Project

Choose a real-world dataset (e.g., finance, health, retail)

Apply full data science pipeline: clean, analyze, model, visualize

Document findings and present insights

Final demo and project report submission

Course Detail

Course Fee

NGN 200,000.00

Course Duration

5 months

Delivery Method

Classroom + Lab + Online + Project

Payment

Flexible payment term. 5% discount on one-time full payment

Course Code

CTE-411

Apply now

Click here to enroll for this course or any other prefered course(s)