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What you will learn
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
Chi-square Test-Of-Association, Goodness-Of-Fit and more. Follow the program syllabus in our course curriculum to know more in detail
About this Course
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.
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!
Google Around 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
Basic Math Skills
Microsoft Excel 2007 or above
Statistics - I
Statistics - II : Types of probability distribution
Hypothesis Testing - I
Hypothesis Testing - II
Multiple Sample Test
Who is this course for?
Students and professionals who wants to apply probability/prediction to solve real world problems
Anyone looking for a career to machine learning and artificial intelligence
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