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Statistics and Probability for Data Sciences
A comprehensive course on Statistics and Probability for Data Sciences.
12 weeks long course | 100% Online
Learn from leading experts in the industry
Project based learning with 2 industry level projects that learners can showcase on LinkedIn.
Learn Key Tools & Technologies Jupyter Notebook
Syllabus
This course is full of best-in-class content by leading faculty and industry experts in the form of videos and projects
Course Overview
- 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.
Course Syllabus
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
Industry Projects
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
Ratings & Reviews by Learners
Skill-Lync has received honest feedback from our learners around the globe.
Instructors profiles
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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