A comprehensive course on Statistics and Probability for Data Sciences.

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This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects

- The students will gain a thorough knowledge of statistics and probability and their use in various machine learning algorithms.
- During the course work, the students will learn about,

- Probability and statistics
- An introduction to,

- Machine learning
- Artificial intelligence
- Deep learning

- Set theory
- Descriptive statistics
- Discrete and continuous probability distribution

- Students are exposed to the modern trends and standard practices followed in the industry right now.

On a daily basis we talk to companies in the likes of Tata Elxsi and Mahindra to fine tune our curriculum.

Week 01 - Introduction to Machine Learning

- This week we will learn about
- Introduction to Artificial Intelligence
- Introduction to Machine Learning
- Supervised, Unsupervised, and Reinforced Learning
- Introduction to Deep Learning
- Modules needed to implement a Machine Learning model

Week 02 - Set Theory

- This week we will learn about
- Set Theory
- Algebra of Sets
- Venn Diagrams

Week 03 - Probability

- This week we will learn about
- Introduction to Probability
- Axioms of Probability
- Independent events
- Mutually exclusive events
- Conditional Probability
- Bayes Theorem

Week 04 - Statistics

- This week we will learn about
- Measures of Central Tendence
- Measures of Dispersion
- Measures of Symmetry

Week 05 - Probability Distribution

- This week we will learn about
- Concept of Random variable
- Bernoulli distribution
- Binomial distribution
- Negative Binomial distribution
- Geometric distribution
- Hypergeometric distribution
- Poisson distribution
- Uniform distribution
- Probability mass function and cumulative distribution function
- Brief intro to Gamma exponential and normal distribution

Week 06 - Continuous Probability Distribution

- In this week, we will learn
- Continuous distributions
- Normal Distribution
- Gamma Distribution
- Exponential Distribution
- Lognormal Distribution
- Weibull Distribution
- F Distribution
- T Distribution
- chi square Distribution
- Probabiltiy Density Function
- Cumulative Distribution Function

Week 07 - Inferential Statistics

- This week we will learn about

- Sampling
- Probabilistic and Nonprobabilistic methods of Sampling Estimation
- Estimation
- Sample size estimation

Week 08 - Hypothesis Testing

- This week we will learn about
- Introduction to hypothesis testing
- Rejection region
- Critical value
- p-value

Week 09 - Hypothesis Testing

- This week we will learn about,

- z - test
- f - test
- t - test
- Anova - test

Week 10 - Non-Parametric Tests

- This week we will learn about
- Chi square test
- Mann Whitney U test
- Kruskal Wallis test
- Sign test
- Correlation
- Chi square
- Karl Pearson
- Spearman Coefficient
- Regression between variables
- Implementation of statistical functions in Jupyter notebook

Our courses have been designed by industry experts to help students achieve their dream careers

Our projects are designed by experts in the industry to reflect industry standards. By working through our projects, Learners will gain a practical understanding of what they will take on at a larger-scale in the industry. In total, there are **2 Projects** that are available in this program.

Analysis of Medical Insurance Data

For this project, learners are to do the following:

- The objective is to find the insurance premium and give the patients' details.
- The data, which contains various parameters such as age, gender, etc., is given to the students.
- The student is expected to perform descriptive statistics and outline the various parameters of the data.
- The student should perform some exploratory data analysis to understand the dataset much better.
- Given the client parameter, the student is expected to find the insurance premium.

Analysis of Fermented Drink Data

For this project, learners are to do the following:

- Analyze the quality of two types of fermented drinks.
- The student will perform descriptive and inferential statistics on the data set.
- The dataset contains the chemical composition of various types of fermented drinks and the ratings they got.

Our courses have been designed by industry experts to help students achieve their dream careers

Skill-Lync has received honest feedback from our learners around the globe.

4.8

Our courses are designed by leading academicians and experienced industry professionals.

1 industry expert

Our instructors are industry experts along with a passion to teach.

9 years in the experience range

Instructors with 9 years extensive industry experience.

Areas of expertise

- Physics

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