Data Science and Analytics is a highly rewarding career that allows you to solve some of the world’s most interesting problems and Statistics the base for all the analysis and Machine Learning models. This makes statistics a necessary part of the learning curve. Analytics without Statistics is baseless and can anytime go in the wrong direction.
For a majority of Analytics professionals and Beginners, Statistics comes as the most intimidating, doubtful topic, which is the reason why we have created this course for those looking forward to learn Statistics and apply various statistical methods for analysis with the most elaborate explanations and examples!
This course is made to give you all the required knowledge at the beginning of your journey, so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips and trick you would require to start your career.
This course provides Full-fledged knowledge of Statistics, we cover it all.
Our exotic journey will include the concepts of:
1. What’s and Why’s of Statistics – Understanding the need for Statistics, difference between Population and Samples, various Sampling Techniques.
2. Descriptive Statistics will include the Measures Of central tendency - Mean, Median, Mode and the Measures of Variability - Variance, SD, IQR, Bessel’s Correction
3. Further you will learn about the Shapes Of distribution - Bell Curve, Kurtosis, Skewness.
4. You will learn about various types of variables, their interactions like Correlation, Covariance, Collinearity, Multicollinearity, feature creation and selection.
5. As part of Inferential statistics, you will learn various Estimation Techniques, Properties of Normal Curve, Central Limit Theorem calculation and representation of Z Score and Confidence Intervals.
6. In Hypothesis Testing you will learn how to formulate a Null Hypothesis and the corresponding Alternate Hypothesis.
7. You will learn how to choose and perform various hypothesis tests like Z – test, One Sample T Test, Independent T Test, Paired T Test, Chi Square – Goodness Of Fit, Chi-Square Test for Independence, ANOVA
8. In regression Analysis you will learn about end-to-end variable creation selection data transformation, model building and Evaluation process for both Linear and Logistic Regression.
9. In-depth explanation for Statistical Methods with all the real-life tips and tricks to give you an edge from someone who has just the introductory knowledge which is usually not provided in a beginner course.
10. All explanations provided in a simple language to make it easy to understand and work on in future.
11. Hands-on practice on more than 15 different Datasets to give you a quick start and learning advantage of working on different datasets and problems.
