Career Objective
Understand Data Science Fundamentals: Gain a solid foundation in data science concepts, including statistics, machine learning, and data analysis techniques.
Learn Data Manipulation and Visualization: Acquire skills in data manipulation using tools like Python and R, and create visualizations to represent data insights effectively.
Develop Data Analysis Skills: Learn to analyze complex datasets, identify patterns, and derive meaningful insights using statistical methods and data mining techniques.
What you will learn?
Mastering Data Science Basics
- Gain a solid understanding of the core concepts and principles of data science.
- Learn about the data science lifecycle, from data acquisition and cleaning to modeling and deployment.
Statistics & EDA
- Apply statistical methods and techniques to analyze data and derive meaningful insights.
- Conduct exploratory data analysis to summarize main characteristics of data, identify patterns, and detect anomalies.
Data Wrangling and Prep
- Acquire skills in data cleaning, transformation, and preprocessing to prepare raw data for analysis and modeling.
- Handle missing data, outliers, and inconsistencies to ensure data quality.
ML Algorithms & Models
- Explore a variety of machine learning algorithms such as linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), and neural networks.
- Understand supervised, unsupervised, and semi-supervised learning techniques for classification, regression, clustering, and dimensionality reduction tasks.
MODULE SEQUENCE TO BE FOLLOWED:
This course includes:
- 40 hours on-demand videos
- Interesting Quizes for every module
- Individual certificates
- Provide Badges
- App Accessissibility
- Full Time Access
- Feedback Mechanism
- Downloadable Materials
- Resource Library
Frequently Asked Questions
Data science is an interdisciplinary field that combines statistical analysis, machine learning, data mining, and data visualization to extract meaningful insights from data.
While having a background in programming is beneficial, it is not mandatory. The course will cover programming basics, primarily in Python and R, and provide you with the necessary skills to get started with data science.
The course will primarily use Python and R for data analysis and machine learning. Additionally, tools such as Jupyter Notebook, pandas, NumPy, scikit-learn, and data visualization libraries like Matplotlib and Seaborn will be used. Big data technologies like Hadoop and Spark may also be introduced.
The course is structured into modules covering various topics, starting with fundamental concepts and gradually progressing to advanced techniques. Each module includes lectures, hands-on exercises, quizzes, and projects to reinforce learning.
You will work on real-world data science projects involving data collection, cleaning, analysis, and modeling. Projects may include predictive modeling, sentiment analysis, recommendation systems, and more, providing practical experience and portfolio-building opportunities.
Basic knowledge of mathematics, particularly statistics and linear algebra, is recommended. Familiarity with programming concepts will also be helpful. However, the course is designed to accommodate beginners and will provide the necessary foundational knowledge.
