
PDF version of the lecture is included in the resources to download for free.
PDF version of the lecture is included in the resources to download for free.
PDF version of the lecture is included in the resources to download for free.
PDF version of the lecture is included in the resources to download for free.
PDF version of the lecture is included in the resources to download for free.
Explore the binomial distribution to calculate probabilities for coin-flip scenarios and factory defects, including exact, less-than, and more-than head counts.
Apply Poisson distribution to model event counts over time, using mean λ to estimate stock needed for five days and assess 10% loss tolerance via cumulative probabilities.
Explore how the hypergeometric distribution models sampling without replacement, using a 196 voter population with 95 males and a sample of ten to calculate the probability of seven males.
Evaluates a normal-distribution test on a small sample (n=13) to detect production issues, using null and alternative hypotheses, a 0.05 significance level, and a p-value from Excel.
Perform a two-sample t-test in Excel to compare before and after training means for color resolution time, decide between equal or unequal variances, and interpret p-values to conclude no improvement.
Learn how to perform one-way and two-way anova to compare multiple levels, interpret p-values, and check independence, normality, and equal variances in data, using a trainer score example.
Learn to run two-way ANOVA with replication using data analysis tools, interpret p-values for interaction and main effects, and apply conclusions about climate and place effects on population size.
perform chi-square goodness-of-fit to validate a binomial distribution by computing counts and expected values. conclude that a p-value less than 0.05 invalidates evidence that the distribution follows a binomial model.
Learn how to build a perceptron in Excel by wiring inputs, weights, sums, and an activation function to produce class probabilities, then update weights via learning rate.
Pre-requisites: Basic knowledge of Python
The Course is Designed from scratch for Beginners as well as for Experts.
*Unlimited update on Questionnaires every month*.
*Updated with Bonus: Machine Learning, Deep Learning with Python - Premium Self Learning Resource Pack Free
Learn the skills of tomorrow, the silicon valley way
Focus on extracting insights from data of any form or shape using a multitude of statistical disciplines for the purpose of creating new products & services or improving the existing ones by predicting the probability in an 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 into 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 2015, the number of posts increased 45% to more than 850,000 tweets per minute.
• YouTube usage has more than tripled in the last two years with users 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 every day.
• Finally, data sent and received by mobile internet users 1500 000TB.
So, with the above examples of how much data gets generated, now how many hidden insights and patterns for accurate future predictions that we can actually achieve by using data science.
According to Forbes, the annual demand for Data Scientist jobs in the United States itself will increase by 364 million by 2020.
The average salary for a Data Scientist is $170,436.
What is the career progression path for data science professionals?
• Data Scientist: with a vast knowledge of Data Science, Machine Learning, and Business Intelligence tools. Data Scientist stands high as Everest.
• Data Analyst: in 2022, the world will generate data 50times more than now, and with each day that passes by the data generated is infinite with that to analyze those data, data analyst jobs will never have to see the face of recession. On LinkedIn itself, there are average 400 new jobs every 12 hours.
• Data Science Trainer: in this present date a lack of knowledge of these advanced 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, the business analyst takes the lead in every business decision-making process of the organization.