DATA SCIENCE
About Course
Data Science is a multidisciplinary field that combines statistics, machine learning, and programming to analyze and interpret complex datasets. It focuses on uncovering meaningful patterns and insights to support strategic decision-making and solve real-world challenges.
Data scientists use tools for data cleaning, exploration, and visualization, while emphasizing the importance of domain expertise. Ethical aspects such as privacy and bias are key considerations, promoting responsible data practices.
As a dynamic field at the crossroads of technology and business, Data Science continues to evolve—driving innovation and transforming industries through data-driven decision-making.
Course Objectives
Pre-requisties
This course is suitable for any IT professional with basic knowledge of:
Computer fundamentals
Programming concepts
Duration
Duration: 4 Months
Class Timing: 90 Minutes per Day
Includes: Access to recorded sessions
What You'll Learn
By the end of the course, you’ll be equipped to:
Perform advanced statistical analysis using tools like Python or R
Develop and deploy machine learning models
Handle large-scale datasets with tools like Hadoop and Spark
Analyze and visualize business data using BI tools such as Tableau, Power BI, or Looker
Understand and apply data privacy regulations and ethical best practices
Use data science for healthcare analytics, such as patient outcomes, disease trends, and clinical research
Who Can Join
IT professionals aiming to transition into Java development or Data Science roles
Students from B.E/B.Tech/B.Sc/M.Sc/M.Tech/BCA/MCA/B.Com backgrounds
Fresh graduates looking to build a career in data-driven technologies
Training Curriculum
Introduction to data science and its significance in various industries.
Understanding the data science lifecycle and workflows.
Core concepts in linear algebra, calculus, and probability.
Statistical principles including hypothesis testing and regression analysis.
Basics of programming with Python or R.
Data analysis and manipulation using libraries like Pandas.
Identifying and handling missing or inconsistent data.
Techniques for data normalization and standardization.
Visualizing data using libraries like Matplotlib, Seaborn, or ggplot2.
Analyzing patterns, trends, and correlations in datasets.
- Introduction to supervised and unsupervised learning techniques.
Evaluation methods such as cross-validation and performance metrics.
Techniques like linear and logistic regression.
Decision trees, random forests, and support vector machines.
K-Means clustering and hierarchical clustering.
Dimensionality reduction using Principal Component Analysis (PCA).
Text preprocessing and fundamental NLP techniques.
Building and evaluating text classification models.
Fundamentals of time series data.
Forecasting using ARIMA and exponential smoothing techniques.
Crafting and selecting important features.
Handling categorical variables and encoding strategies.
Deploying machine learning models into real-world applications.
Integrating models into production systems and pipelines.
Introduction to distributed computing using Hadoop and Spark.
Techniques for handling and processing large-scale datasets.
Understanding the impact of bias and ethical implications.
Promoting fairness, transparency, and responsibility in data science.
Hands-on project to implement end-to-end data science solutions.
Final presentation and peer reviews of project work.
Deep learning concepts and neural network basics.
Reinforcement learning and recent innovations in AI/ML.
Real-life examples of data science applied in domains like healthcare, finance, and retail.
Case studies exploring successful implementation strategies.
Staying up to date with evolving tools, platforms, and trends.
Collaborating and networking within the data science community.