# Statistics Foundation Self-Study Course

Statistics for Data Science

#### Get the Detailed Course Curriculum & Invite for Online Counselling Session!

Limited Seats Available!
Date : 23rd May, 2018 (Wed)
Time :

Why Study Statistics for Data Science?

Statistics is the cornerstone of Data Science. Only when you know the various statistical techniques used in analysis, would you be able to use them. Statistics provides a foundation for analyzing the performance of a research method and that’s not limited to use in just science, but it has been of widely applied in other industries like Finance, Logistics and Marketing.

This subject is a fundamental ingredient in the skillset of a Data Scientist in the modern day. It is only the specific functions of Statistics for Data Science that you need to master and our free statitics course gives you just that.

## Highlights of Statistics For Data Science Foundations Course

• Video based Self-Paced Course, lets you learn at your own pace
• Know all the vital concepts that you’ll use while working with data
• This course will cover a range of topics ranging from descriptive statistics, probability, distributions, estimation, hypothesis testing, inference to regression
• You can use the techniques learnt in this course irrespective of the tool you plan to use for Data Science & Analysis

• The Admission process is simple, just enroll here and we will do the needful
• There are no prerequisites for taking this course. Anyone with a basic understanding of mathematics is free to register
• There are no timelines, you can start anytime

Price: ₹ 4,900 ₹ 0
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### Statistics for Data Science Course Syllabus

Data and Statistics

– Elements, Variables, and Observations
– Scales of Measurement
– Categorical and Quantitative Data
– Cross-Sectional and Time Series Data
– Descriptive Statistics
– Statistical Inference
– Descriptive Statistics: Tabular and Graphical
– Summarizing Categorical Data
– Summarizing Quantitative Data
– Cross Tabulations and Scatter Diagram

Descriptive Statistics: Numerical Measures
– Measures of Location
– Measures of Variability
– Measures of Distribution Shape, Relative Location, and Detecting Outliers
– Box Plot
– Measures of Association Between Two Variables

Introduction to Probability
– Experiments, Counting Rules, and Assigning Probabilities
– Events and Their Probabilities
– Complement of an Event
– Independent Events
– Multiplication Law
– Baye’s theorem

Discrete Probability Distributions

– Random Variables
– Discrete Probability Distributions
– Expected Value and Variance
– Binomial Probability Distribution
– Poisson Probability Distribution

Continuous Probability Distributions

– Uniform Probability Distribution
– Normal Curve
– Standard Normal Probability Distribution
– Computing Probabilities for Any Normal Probability Distribution

Sampling and Sampling Distributions

– Sampling from a Finite Population
– Sampling from an Infinite Population
– Other Sampling Methods
– Stratified Random Sampling
– Cluster Sampling
– Systematic Sampling
– Convenience Sampling
– Judgment Sampling

Interval Estimation

– Population Mean: Known
– Population Mean: Unknown
– Determining the Sample Size
– Population Proportion

Hypothesis Tests

– Developing Null and Alternative Hypotheses
– Type I and Type II Errors
– Population Mean: Known
– Population Mean: Unknown

Inference About Means and Proportions with Two Populations

– Inferences About the Difference Between Two Population Means
– Inferences About the Difference Between Two Population Means
– Inferences About the Difference Between Two Population Means
– Inferences About the Difference Between Two Population

– Inferences About a Population Variance
– Inferences About Two Population Variances

Tests of Goodness of Fit and Independence

– Goodness of Fit Test: A Multinomial Population
– Test of Independence

Simple Linear Regression

– Simple Linear Regression Model
– Regression Model and Regression Equation
– Estimated Regression Equation
– Least Squares Method
– Coefficient of Determination
– Correlation Coefficient
– Model Assumptions
– Testing for Significance
– Using the Estimated Regression Equation for Estimation and Prediction
– Residual Analysis: Validating Model Assumptions
– Residual Analysis: Outliers and Influential Observations

Multiple Regression

– Multiple Regression Model
– Least Squares Method
– Multiple Coefficient of Determination
– Model Assumptions
– Testing for Significance
– Categorical Independent Variables
– Residual Analysis

Time Series Analysis and Forecasting

– Time Series Patterns
– Forecast Accuracy
– Moving Averages and Exponential Smoothing
– Trend Projection
– Seasonality and Trend
– Time Series Decomposition

Nonparametric Methods

– Sign Test
– Wilcoxon Signed-Rank Test
– Mann-Whitney-Wilcoxon Test
– Kruskal-Wallis Test
– Rank Correlation
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## INDUSTRY EXPERTS SPEAK

#### Ravi Vijayaraghavan

Creating a talent pool in India with Practical hands-on experience in Analytics and Data Science is the need of the hour. Platforms like Digital Vidya are critical to filling this gap.

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President and Head – Analytics and Decision Sciences

#### Lovekesh Vig

Digital Vidya has a very important role in getting the word out from the industry’s perspective. Initiatives like Digital Vidya will hopefully help to plug the gap between academia & practical skills.

Lovekesh Vig
Senior Scientist

#### Ambuj Kathuria

Digital Vidya is doing a great job of bringing people from diverse set of experiences to one platform for creating the best of Data Science skill pipeline.

Ambuj Kathuria

#### Akshay Sehgal

Digital Vidya is doing a great job at bringing data analytics to the rest of the world!

Akshay Sehgal
General Manager

#### Naresh Mehta

Good to see Digital Vidya becoming increasingly more involved in covering data science vertical, look forward to collaborate with DV to help shape this industry.

Naresh Mehta
AVP Data Science & Analytics

## DIGITAL VIDYARTHIS SPEAK

#### Faisal Hussain

Deputy Manager / Enzen Global Solutions Pvt Ltd

It was really an interactive Data Analytics training experience. It will surely help me in starting up my analytics career.

#### Utathya Ghosh

Data Analyst / Right Relevance

Really peaked my interest in Tableau. It is a fun tool and the presenter did a great job to help me learn. Presenter has good knowledge of subject and works well with students.

#### Amit Kumar Sinha

General Manager / Leela's Foundation For Education & Health

Came to know new and advanced technical knowledge about Excel and Power BI platform.

#### Hareesh Kumar

Software Development Engineer / Mcafee(Intel Security)

The attachment with Digital Vidya is memorable.Thanks

#### Dhanashree Bagal

Associate Software Engineer / Accenture

It was good learning with digital Vidya. The whole team is supportive. Trainer was able to answer almost every asked question.

#### Nanddeep Nasnodkar

Sr. Software Developer / Remote Software Solutions

This course gets you started from very basics, makes you think and solve the assignments, and suddenly you find yourself doing Data Analytics all by yourself!

#### Ranjan Kumar

Map Analyst / Lionbridge Tech

I would like to sincerely offer my thanks and gratitude to Digital Vidya for providing me with the most efficient and relevant mentors and course materials to learn Data Analytics and R. I would specifically like to mention weekly assignments during my course tenure to learn R were the best.

#### Vani Ananthamurthy

Business Operations Senior Analyst / Accenture

I was looking for customized content and I found the same in Digital Vidya. Content is structured and well planned. Classes were very interactive and trainer’s presentation skills were very good. People who are new to the subject can also understand clearly. Thank you so much!

India
VP, Data Science

#### Vishal Mishra

CEO & Co-Founder, Right Relevance

#### Ajay Ohri

Sr. Data Scientist at Kogentix Inc.

#### Manas Garg

Director of Engineering, PayPal Inc

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