Data Science Course Track

Why Us

Why Data Science?

Data Science is projected to grow in demand by 28% by the year 2020 (source: report by IBM). In its 2017 U.S Emerging Jobs report, LinkedIn ranked Machine Learning Engineers, Data Scientists and Big Data Engineers among the top emerging jobs.

Importance of DS

Data Science is now being integrated with industries across all sectors. That’s why not only are the Data Scientists expected to have a broader set of skills, but the employers also expect more cohesive specialization and collaboration.

Industry Collaboration

Entire course has been vetted and created along with experts in Data Science industry, ensuring relevance with industry setting. Our Trainers and Consultants have worked with organizations like HSBC, Berkshire Hathaway, Canara HSBC OBC Life Insurance.

Real Life Projects

All projects in course are based on real life Data Science problems. No academic datasets are used ensuring you're ready for real life problems. Datasets are samples from industry we have experience working with, making sure it's tailored for training purposes.

Who Should do the Course?

Fresh Graduates, Engineering Students, Project Managers, Business Managers, Senior Leaders or people from any field who are willing to transition to Data Sciences.
Young professionals and Managers who have little or no formal education in Business Analytics.
Executives with analytical aptitude who are interested in and want to learn Data Analytics through hands on practice on popular tools.
Individuals who aspire to switch or embark on a career in Business Analytics or Data Science.
Professional from any domain who have logical, mathematical and analytical skills.

Get the hands on learning of Business Statistics, Probability Distributions, R Programming, Tableau and Exploratory Data Analysis.

Assignment - Probability Distribution

Data Types
Continuous Variables
Ordinal Variables
Categorical Variables
Miscellaneous
Sampling
Need for Sampling?
Different types of Sampling
Simple Random Sampling
Systematic sampling

Stratified Sampling
Descriptive statistics
Data Distributions
Normal Distribution – Characteristics of a Normal Distribution
Binomial Distribution
Hypothesis Testing
Type-I error
Type-II error
Null and Alternate Hypothesis
Reject or acceptance criterion

Assignment - Statistics & Probability Distributions

Data Exploration and Preparation
Variable Identification
Univariate Analysis - The What and How?
Univariate Analysis - The Measure of Central Tendency
Univariate Analysis - Measure of Dispersion, Range, Quartile, Inter Quartile Range(IQR)
Variance, Population Variance, Sample Variance, Standard Deviation, Skewness, Kurtosis

Assignment - Introduction to Exploratory Data Analysis

Assignment - Introduction to R

Getting data into R- reading from files, connecting to DB
Cleaning and preparing the data – converting data types (Character to numeric etc.,)
Handling missing values – Imputation or replacing with place holder values.
Visualization in R using ggplot2 (plots and charts) – Histograms, Bar charts, Box plot, Scatterplots.
Adding more dimensions to the plots – Geom. (), Dodge etc.
Visualization using Tableau (Introduction).
Correlation – Positive, Negative and No-correlation.
What is a spurious correlation.
Correlation Vs. Causation.

Assignment - Exploratory Data Analysis Using R Programming

Connect multiple slicers to the pivot tables.
Explore in more depth the full power of pivot tables.
See how to filter the data shown in the pivot in many ways to achieve interesting subsets of the data.
Use calculated fields on top of the pivot table to calculate profitability and find anomalies.
Use formulas to aggregate the data as an alternative to pivot tables for more flexible reporting layouts.
See how a pivot can use more than one table and introduction to the Excel data table.

Assignment - Excel for Data Analytics

Assignment - Tableau and Products

Assignment - Basic Charts on Tableau

Different types - Prediction, Forecasting, Optimization, Segmentation etc.
Supervised Learning
Linear Prediction
Simple Linear Regression
Model development and interpretation
Sum of least squares
Model Validation – tests to validate assumptions
Disadvantage of Linear Models.
Multiple Linear Regression.

Assignment - Predictive Analytics

Welcome
Getting Started
Resume Layout
Tagline
Your Skills
Your Education
Your Experience
LinkedIn and Cover Letter
How to ask for recommendations
Concluding thoughts.

Handholding - Let's create your personalized Resume

TESTIMONIAL

“The consultants were very helpful and explained everything in great detail. They also accommodated our short timeframe.” Customer Service Manager
A Leading Healthcare Company.

“Though my background is in engineering, I was unclear about the next steps to take and my career path. After in-depth discussions with experts at Wenso Training, I took up training in Selenium.
I am now working as a QA Analyst in Birmingham, UK.”

“I was working as a Test Engineer and was looking for courses to enhance my skills. One of my friends directed me to Wenso Training where I undertook training in BEHAT and Cucumber. The knowledge gained from the training not only elevated my skills, but it also helped me to progress into a new role.”

“Though my background is in computer science engineering & I am still in penultimate year of my program,I was unclear about the next steps to take in my career path. After in-depth hand-holding & training with experts at Wenso Data Sciences, I have already done couple of research projects & I hold admissions from some of the top German universities at the moment.”

“I worked as a Business Analyst and was looking forward to transition my career in Data Sciences.One of my friends from past cohort directed me to Wenso Training, where I pursued Data Mining & Machine Learning course tracks.The knowledge gained from the training elevated my Analytics skills.”

“The course had a lot of information (which made it overwhelming at times) but it was presented very well.I liked all the practical examples and exercises. Thank you for a great learning experience. I have successfully transitioned into a new Data Analyst role, currently working with a leading healthcare company in UK.”

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