Data Science
Learn how to transform data into actionable insights through hands-on training in Python, statistics, machine learning, and AI. Build the skills needed for today's most in-demand data careers.
Data Science
- Intermediate
- 14 Weeks
- Hybrid
Master the complete data science workflow, from data collection and analysis to machine learning and predictive modeling. Learn Python, statistics, data visualization, and industry-standard tools to transform raw data into actionable insights and intelligent solutions.
This program equips learners with practical skills in data analysis, statistical modeling, and machine learning through hands-on projects and real-world datasets. Build a portfolio that demonstrates your ability to solve business problems using data.
What You'll Learn
Develop practical data science skills through real-world datasets, machine learning projects, and industry-standard tools.
Python for Data Science
Work with Pandas, NumPy, and visualization libraries.
Statistics & Analytics
Apply probability, regression, and hypothesis testing.
Machine Learning
Build predictive models using Scikit-learn.
Projects & Deployment
Create portfolio-ready data science solutions.
CURRICULUM
01. Python & Data Science Foundations
Python for data science: syntax, data structures, functions, and object-oriented programming
NumPy: arrays, broadcasting, vectorized operations, and numerical computing
Pandas: DataFrames, data cleaning, transformation, merging, and analysis
Jupyter Notebooks: workflows, documentation, reproducible research, and experimentation
02. Statistics & Data Visualization
Descriptive statistics: mean, median, variance, standard deviation, and distributions
Inferential statistics: hypothesis testing, confidence intervals, and p-values
Data visualization with Matplotlib and Seaborn for effective storytelling
Exploratory Data Analysis (EDA): identifying patterns, trends, and anomalies in data
03. Machine Learning
Supervised learning: linear regression, logistic regression, decision trees, and random forests
Unsupervised learning: clustering, dimensionality reduction, and pattern discovery
Model evaluation: accuracy, precision, recall, F1-score, ROC-AUC, and cross-validation
Feature engineering, model optimization, and handling real-world datasets
04. Advanced Topics & Capstone Project
Ensemble methods: boosting, bagging, XGBoost, and LightGBM
SQL for Data Science: querying, transforming, and preparing data for analysis
Model deployment with Flask/FastAPI and API-based prediction services
Capstone project: build and deploy an end-to-end machine learning solution using a real-world dataset
Data Science
- Course Rating
4.8 Stars
- Certificate
Yes, certificate of completion.
Tools & Technologies
- Why Choose NexEdge
Why Learners Choose NexEdge For Career Growth
Focused on practical learning, industry exposure, and career-focused programs designed to prepare learners for real-world opportunities.
Industry-Focused Curriculum
Programs designed around real industry tools, trends, and skills.
Hands-On Learning
Practical projects and real-world exposure to improve confidence.
Expert Trainers
Learn from experienced professionals with industry expertise.
Placement Support
Career-focused guidance and support for future opportunities.
Flexible Learning Modes
Online, offline, and hybrid training designed for modern learners.
- Contact Form
Start A Conversation With Us
Email Support
info@nvvnexedge.com
Business Hours
Monday – Saturday, 09:00 – 17:00
