Data Science Using Python
The role of a data scientist stands out as one of the most well-matched careers to prosper in this era. It is characterized by its digital nature, emphasis on programming, and analytical focus. Consequently, it is not unexpected that the need for data scientists has been rapidly increasing in the employment landscape.
What you'll learn
- Basic Concept of Statistics
- Concept of Descriptive Statistics
- Concept of graphs and Chart
- Concept of Statistical Distribution
- Concept of Hypothesis Testing
- Different type of statistical test
- Concept of Linear Regression Model
- Concept of Logistic Regression Model
- Basic Data Preparation process
Requirements
No prior experience is required. We will start from the very basics
Microsoft Excel 2003, 2010, 2013, 2016, or 365
Course description
Helps to understand the basic concept of analytics with the data set structure and various types of variables which are widely used to day to day work.
Helps to understand the basic concept of descriptive statistics and how to use those techniques to get meaningful insight from any given data.
Helps to understand different types of graphical presentation which are very helpful for presenting any data set with meaningful information in a very easiest manner.
Helps to understand how statistical distribution can be checked for any given set of data and how any given hypothesis or assumption of any data set can be tested with the help of statistical test to solve business problem.
Helps to understand how to use multiple linear regression method to identify the importance of predictors and also to make prediction for future.
Helps to understand how to use logistics regression method to identify the importance of predictors for an Binary dependent variable and also use the model for classification purpose.
Helps to understand different types of data processing techniques which are very useful to perform any statistical test or reports.
Who this course is for:
- If you aspire to pursue a career as a Data Scientist or if you seek to gain insights into the field.
- This program is tailored for individuals seeking a rewarding professional path.
Course content
- Introduction of Analytics/Data Science
- Basic Steps of Analytics
- Data definition
- Variable definition
- Mean/Median/Mode
- Standard Deviation/Variance
- Coefficient of Variation
- Covariance Analysis
- Correlation Analysis
- Histogram
- Box Plot
- Scatter Plot
- Line Diagram
- Bar Diagram
- Pie Chart
- Normal Distribution
- Definition of Hypothesis testing
- Null hypothesis vs Alternative Hypothesis
- Type of Error
- Significance value
- p value
- t-test: One sample t test, Two sample t test, Pair t test
- ANOVA
- Chi_Square test
- Basic concept of Regression analysis
- Introduction of Simple linear regression
- Example of simple linear regression
- Introduction of Multiple linear regression
- Example of multiple linear regression
- Linear regression model validation process
- Prediction using Linear regression equation
- Basic Concept of Logistic Regression
- Different components of Logistic Model
- Model result interpretation using odd ratio
- Model validation process using: Recall, Precision, Accuracy, AUC
- Identify the missing value in data set
- Identify the duplicate row in data set
- Identify the outliers in data set
- Conversion of any numerical variable to categorical
- Normalization of variables
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English
30-35 Hours
Recorded Videos
Full lifetime access
Certificate of Completion