# Data Science Course

Learn Mathematics Functions, Statistics and Linear Regression model concept in Data Science with Python

Hello, guys welcome to the Data Science course. In this course, I have summarized the essential concept necessary to be a master in Data Science. I have tried my best to give a detailed explanation and a clear demonstration of how well data is represented.

What you’ll learn

• Data Cleaning and Preparation.
• Linear Functions and other Data Science Mathematics.
• Statistics.
• Data Representation and Interpretation.

Course Content

• Introduction –> 4 lectures • 15min.
• Data Science Introduction –> 10 lectures • 58min.
• Mathematics Functions for Data Scientist –> 4 lectures • 16min.
• Statistical Analysis Tools –> 5 lectures • 17min.
• Data Science Advanced Tools –> 5 lectures • 22min.

Requirements

Hello, guys welcome to the Data Science course. In this course, I have summarized the essential concept necessary to be a master in Data Science. I have tried my best to give a detailed explanation and a clear demonstration of how well data is represented.

Data Science has become more prevalent in all significant areas like the Health sector, financial market, marketing teams, accounting and manufacturing industries. Data science gives accurate predictions on analyzed data and gives clear outcomes for the organization to make decision-based on the analysis which has been made. In this course, I have demonstrated how to gather data from various sources then clean the data and make analysis for decision-making. In this course there are Mathematical Analysis functions, Statistical Analysis tools and Advanced Data Science tools which I combined in demonstrating how powerful and effective Data Science is when analysing data. These tools and functions are used in various sectors across the globe when analysing the data pattern.

I have presented this course content clearly with the inclusion of all Beginner level in Python Programming and Data Science students who are still fresh, I did so to make sure that we all understand the lectures well. As a Data analyst, it is important to make sure that you isolate your dependent and independent variables within your dataset and then clean the data to remove all empty cell and Nan values.

In this course, we shall use Jupyter Notebook, but I shall demonstrate on how to use Google Colab as your number one priority IDE for Data Science because there is no configuration needed there to do your Analysis. However, we are going to use Anaconda and I shall demonstrate how to set up the environment to be ready for data analysis. This course is for Beginners in Data Science.

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