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Planet of data science is here to provide you the knowledge of data science.

This data science with Python course will help you learn the basics of Data science along with different and easy steps of data science such as statistics, data visualization, data preprocessing, making machine learning models, and much more with the help of well-explained examples. This course will help both beginners as well as some trained professionals in learning data science with Python.

Statistics, Machine Learning Education are most important and essential skill for any learning data analyst and data scientist, and also for those who wish to transfer a big amount of raw data into visuals and predictions. Learn this skill today with Planet with data science.

Introduction to Data Science:

In a world of data space where Industries deal with huge amount of data, the era of Big Data started, the priority of its storage also grew. It was a big challenge and concern for organizations for the storage of data until 2010. At that time this frameworks like Hadoop and other software's solved the problem of storage, the focus converted to processing of data. At this point of time, Data Science plays a big key role here. All the high-scaled movies and user friendly software's you love to see around can turn into reality by Data Science. Nowadays it’s Data science has been increased in different ways and thus each should be ready for our future by learning what it is and how can we add value to it and to our life.


1. Taking input in Python

2. Output using print() function

3. Variables in Python

4. Expression Condition in Python

Data Types In Python:

              a. Strings

          b. Lists

          c. Tuples

          d. Sets

          e. Dictionary

          f. Arrays

5. Functions in Python

6. Operators in Python

7. Loops

8. Control Statements


1. Measures of Central Tendency

2. Normal Distribution

3. Binomial Distribution

4. Bernoulli Distribution

5. Poisson Discrete Distribution

6. P-value

7. Correlation in Python

8. Pearson’s Chi-Square Test


1. Linear Regression

2. Logistic Regression

3. Naive Bayes Theorem

4. Decision Trees

5. Random Forest

6. K-nearest neighbor(KNN)

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