Learn the skills of tomorrow,

the silicon valley way

Focus on extracting insights from data of any form or shape using multitude of statistical disciplines for the purpose of creating new products & services or improve the existing ones by predicting its probability in a event. And as the enormity of data is on the rise, there is a desperate need for professionals with data science skills to get valuable insights on it. According to NYTimes there are fewer than 10,000 qualified people in the world and universities are only graduating about 100 candidates each year.
Learn the skills of tomorrow,

What you'll learn

This course features on:

  • Silicon Valley Way

    Learn and master data science with cutting edge open source R and Excel tools.

  • In-Demand Skills

    Learn with 42 modules how to automate more than 17 data science algorithms without any coding.

  • Real World Case Studies

    Get hands on experience on real world examples on how to solve problems

Learning Outcome

Full Guide to implementing data science algorithms without a single line of coding in Excel and R statistical programming language.

  • Learn the interdisciplinary concepts of data science with help of success stories and master the typical stages of Solution Implementation with data exploration, preparation, partitioning, model building, model iterations and validation.

  • Learn Inferential statistics and summarize a set of observations using Summary Statistics to identify a single number that describes the entire dataset by using the types of Measures of Central Tendency and differentiate the extend a distribution of data is stretched or squeezed by using Measures of Dispersion.

  • Know how to perform: Binomial, Poisson, Hyper Geometric, Negative Binomial, Geometric discrete probability distributions also learn and implement Normal distribution and T-distribution of continuous probability distribution.

  • Perform hypothesis testing with Normal Distribution and T-distribution using One-Tail and Two-Tail Directional hypothesis even if the sample size is low or the standard deviation is not available or again if the population distribution is not Normal Distribution.

  • Learn how to perform One-Way and Two Way anova for multiple levels or factors influencing the outcome with and without replication and for count and categorical data using Chi-square Test-Of-Association & Goodness-Of-Fit

  • Follow our program syllabus in course curriculum to know more in detail.

Featured Instructor

Our expert instructors are here to support you

  • Rupak Roy

    Data Scientist

    Rupak Roy

    Mr. Roy also popularly known as Bob is the Co-founder of Data2Dimensions, a site for an easy and complete actionable intelligence based on AI, Big Data, IoT for business growth needs with a mission "We Rise By Lifting Others". He developed D2Academics for students as well as for professionals for a successful career move tailored by cutting edge silicon valley technologies. His recent development includes D2Forms to connect people with data and discover synchronicity that leads one thing to another.

    "Imagine a privilege to do what we love and do it in a way that can actually connect people directly in a profound way." -- Rupak Roy

Course curriculum

  • 1
    Welcome to the course!
  • 2
    Introduction
  • 3
    Methodology
  • 4
    Statistics
  • 5
    Types of probability distribution
  • 6
    Hypothesis Testing - I
  • 7
    Hypothesis Testing - II
  • 8
    Multiple sample test
  • 9
    Thank you
  • Welcome to world of A.I. From Syria to Canada FREE PREVIEW How to use this course Data Science Syllabus FREE PREVIEW Before we begin...
  • Introduction To Data Science FREE PREVIEW Introduction To Data Science
  • Analytics and solution implementation Analytics and solution implementation (pdf) Test you learning
  • Types of Statistics Types of Statistics - II Types of Statistics (pdf) Test your learning A.I. Making Sense of a Messy World
  • Binomial Distribution Poisson Distribution Hyper-Geometric FREE PREVIEW Normal Distribution Probability Distribution in R Types Of Probability Distribution (pdf) Types of probability distribution. Lab 1 Types of probability distribution. Lab 2 Types of probability distribution. Lab 3 Types of probability distribution. Lab 4 Test your learning
  • Hypothesis Test T-Dist Hypothesis Testing (pdf) Hypothesis Testing. Lab I Hypothesis Testing. Lab - II Sample dataset for practice Test your learning
  • Directional Hypothesis 2 Sample Test FREE PREVIEW Paired Difference T-test T-test in R Hypothesis Testing - II (pdf version) Hypothesis Testing - II Lab 1 Hypothesis Testing - II Lab 2 Take a tour on how A.I. is addressing the world. Test your learning Sample dataset for practice
  • One way ANOVA Two way ANOVA Chi-square: Test-Of-Association Chi-square: Goodness-Of-Fit Multiple sample test (pdf) Test your learning
  • More resources for you Before you go... Tania’s Story: Morse code meets machine learning. Congrats! Here's what's next...

Pricing options

Join the jobs of tomorrow with a minimum fee of $799 ~ 199 HURRY! Join and share "We Rise By Lifting Others" offer.

Bonus material

In addition to full course access, you will also receive:

  • Free Course content

    We believe one of the core strengths of a training is flexibility, thus we have designed the course modules in a way that the contents that you will learn you can download it at free of cost, so that you can access your course contents and learn it at your own pace anywhere, anytime, anyplace.

  • Extra study materials

    Keeping in mind that knowledge has a beginning but no end, we bring you additional eBooks, articles, references, blogs etc. to leverage you with extra benefits to your overall learning experience.

  • 1-year hands-on experience

    Recruiters around the globe are increasingly seeing the value of hands-on experience rather than just learning from just a text books. To meet the fasting changing industrial needs the course contents are designed intensively with industry projects to keep you ahead of any challenges for a fast growing industrial needs.

What people are saying

by Muhammad Umair

Methodology of Data Analysis

by Muhammad Umair

Methodology design has delivered in such a way that I despite of having another background, capable of answering the questions and got 90% marks.
Soledad Perez

Informative

Soledad Perez

I loved the experience, I felt I did not study it amused me with all the new information, which is so interesting.

Nevia K

Initially the course starts with the basics than in the lab session the tools and techniques used are really applicable for my day to day business solutions. Very interesting and appreciated.

Advance your AI career with:

Frequently Asked Questions (FAQ):

  • Who is the targeted audience?

    • Students and professionals who wants to apply probability/prediction techniques to solve real world problems.
    • Anyone looking for a career to machine learning and artificial intelligence (AI)

  • Minimum Requirements

    • Basic Math Skills
    • Microsoft Excel 2007 or above
    • R Studio (included with the course)

  • Why data science is so important?

    • Twitter Since 2013, the number of posts increased 25% to more than 350,000 tweets per minute.
    • YouTube usage has more than tripled in the last two years with user uploading 400 hours of new video each minute of every day.
    • Instagram users like 2.5 million posts every minute!
    • GoogleAround 4 million Google searches are conducted worldwide each minute of everyday.
    • Finally , data send and received by mobile internet users 1500 000TB.
    So, with the above examples of how much data gets generated, now how much hidden insights and patterns for accurate future predictions that we can actually achieve by using data science.

    According to Forbes, annual demand for Data Scientist jobs for United States itself will increase by 364 million by 2020. The average salary for a Data Scientist is $113,436.

  • What are the career progression path for data science professionals?

    • Data Scientist: with a vast knowledge of Data Science, with Machine Learning and Business Intelligence tools. Data Scientist stands high as the Everest.
    • Data Analyst: in 2019, the world will generate data 50times more than now and with each day passes by the data generated is infinity and with that to analyze those data, data analyst jobs will never have to see the face of recession. In Linkedin itself there are average 400 new jobs for every 12 hour
    • Data Science Trainer: in this present date with a lack of the knowledge of these advance data science techniques gives a vast opportunity to become the fountain of data science for others.
    • Business analyst: with the role of defining and managing the business requirements, business analyst takes the lead in every business decision making process of organization.