What will you learn in Data Science Training Course
Master Python, R, and SQL for data analysis, machine learning, and statistical modeling
Explore data visualization tools like Tableau, Power BI, and Python libraries (Matplotlib, Seaborn)
Build machine learning and deep learning models using Scikit-learn, TensorFlow, and Keras
Handle big data using Hadoop, Spark, and real-time streaming tools like Kafka
Apply data science to real-world business problems with end-to-end projects
Prepare for top industry certifications and job roles in data science and AI
Program Overview
Module 1: Python for Data Science
⏳ 2 weeks
Topics: Python basics, data structures, libraries like NumPy and Pandas
Hands-on: Perform exploratory data analysis and build Python-based data scripts
Module 2: Statistics & Probability
⏳ 2 weeks
Topics: Descriptive stats, inferential stats, probability distributions
Hands-on: Analyze datasets using statistical tests and confidence intervals
Module 3: Machine Learning with Scikit-learn
⏳ 3 weeks
Topics: Supervised, unsupervised learning, model evaluation
Hands-on: Build classification, regression, and clustering models
Module 4: Deep Learning with TensorFlow & Keras
⏳ 3 weeks
Topics: Neural networks, CNNs, RNNs, activation functions
Hands-on: Train and evaluate deep learning models on image/text data
Module 5: R Programming for Data Science
⏳ 2 weeks
Topics: Data frames, dplyr, ggplot2, statistical modeling
Hands-on: Perform data analysis and visualization using R
Module 6: SQL for Data Science
⏳ 1.5 weeks
Topics: Joins, aggregations, subqueries, window functions
Hands-on: Query structured data for analysis and reporting
Module 7: Data Visualization with Tableau & Power BI
⏳ 2 weeks
Topics: Dashboards, filters, charts, calculated fields
Hands-on: Build interactive business dashboards from raw data
Module 8: Big Data & Spark for Data Science
⏳ 2 weeks
Topics: Hadoop ecosystem, Spark RDDs, Spark MLlib
Hands-on: Process and analyze large datasets using PySpark
Module 9: Capstone Project
⏳ 2 weeks
Topics: End-to-end data science case study involving real-world datasets
Hands-on: Apply data science lifecycle: data wrangling, modeling, evaluation, visualization
Get certificate
Job Outlook
Data Scientists are among the most sought-after professionals globally
Career roles include Data Scientist, Machine Learning Engineer, and AI Specialist
Salaries range from $100,000 to $160,000+ in top markets
Strong demand in sectors such as healthcare, fintech, e-commerce, and consulting
Explore More Learning Paths
Advance your data science skills with these carefully selected courses designed to deepen your understanding of data analysis, tools, and methodologies for real-world applications.
Related Courses
Foundations of Data Science Course – Build a strong foundation in data analysis, statistics, and programming essentials to prepare for advanced data science projects.
Tools for Data Science Course – Learn to work with key data science tools and technologies to efficiently process, analyze, and visualize data.
Data Science Methodology Course – Understand structured approaches and methodologies to solve complex data science problems effectively.
Related Reading
What Does a Data Engineer Do? – Explore the role of data engineers in managing, structuring, and optimizing data pipelines that support data science workflows.
Specification: Data Science Training Course
|
FAQs
- Covers foundational programming with Python, R, and SQL.
- Teaches machine learning, deep learning, and big data tools like Hadoop and Spark.
- Includes hands-on projects for real-world data analysis and predictive modeling.
- Prepares learners for industry certifications and job roles like Data Scientist, ML Engineer, and AI Specialist.
- Focuses on end-to-end data science workflow from data wrangling to visualization.
- Teaches Hadoop ecosystem, Spark RDDs, and Spark MLlib for large dataset processing.
- Covers real-time data streaming with tools like Kafka.
- Includes exercises for processing and analyzing big datasets using PySpark.
- Guides learners in building scalable data pipelines for enterprise applications.
- Prepares learners to tackle big data challenges in various industries.
- Designed for beginners but familiarity with basic math or programming is helpful.
- Introduces Python, R, and statistical concepts progressively.
- Provides step-by-step exercises to strengthen programming and analytical skills.
- Encourages hands-on practice to build confidence in data manipulation and modeling.
- Suitable for career changers, freshers, and aspiring data professionals.
- Covers supervised and unsupervised machine learning using Scikit-learn.
- Teaches deep learning with TensorFlow and Keras for neural networks, CNNs, and RNNs.
- Provides hands-on exercises to train and evaluate models on image, text, and tabular data.
- Guides learners in model selection, evaluation, and deployment strategies.
- Prepares learners for AI-focused roles and projects in real-world business contexts.
- Covers Tableau and Power BI for interactive business dashboards.
- Teaches Python libraries like Matplotlib and Seaborn for data visualization.
- Guides learners in creating charts, filters, calculated fields, and dashboards.
- Focuses on turning data insights into actionable visual reports.
- Prepares learners to communicate data findings effectively to stakeholders.
