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Data Science with Python and R Course Info

This 110-hour course teaches data science using Python and R. Participants will learn data science concepts and gain practical experience in both programming languages. This Data Science with Python and R Course transcends theoretical knowledge, offering hands-on experience through practical exercises and real-world projects. Graduates emerge with a versatile skill set, ready to tackle complex data challenges in various industries.

Take advantage of our flexible EMI options, a detailed data science course syllabus, and receive certification after finishing the course. Start your career on the right path with the top-notch Data Science with Python and R Course.

We offer a data science course in Bangalore for both beginners and experienced professionals. Our course is designed to meet your needs and enhance your data science skills to the next level. Our team of industry experts will provide you with high-quality training and guidance throughout the course.

The hourly breakdown is indicative and may vary based on the pace of the class and additional interactive activities.

Practical exercises, case studies, and real-world applications should be incorporated throughout the sessions to reinforce learning.

Data Science with Python and R Course Content

Lesson 1: Introduction to Python and Jupyter Notebooks
Lesson 2: Data types, variables, and operators
Lesson 3: Control flow: if statements, loops, and functions
Lesson 4: Data manipulation with Pandas
Lesson 5: Data visualization with Matplotlib and Seaborn

Lesson 1: Descriptive statistics and probability
Lesson 2: Hypothesis testing and inferential statistics
Lesson 3: Exploratory Data Analysis (EDA) techniques
Lesson 4: Feature engineering and selection

Lesson 1: Overview of machine learning concepts
Lesson 2: Types of machine learning algorithms
Lesson 3: Model evaluation and hyperparameter tuning
Lesson 4: Practical implementation of ML models

Lesson 1: R syntax and data structures
Lesson 2: Data manipulation and visualization with ggplot2
Lesson 3: Statistical modeling using R

Lesson 1: Project definition and scoping
Lesson 2: Implementation of end-to-end data science solution
Lesson 3: Project presentation and feedback

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