DATA SCIENCE
DATA SCIENCE
According to the recent IBM prediction there will be around 30% increase in the employment of data scientists in upcoming two years.
Here, at Prolytics you will be trained with detailed learning in data science, data analytics, project life cycle, analysis, data acquisition, statistical methods and machine learning.
If you are Big Data Specialist, BI professionals, Business Analyst, Statistician, Information Architects or want to learn Machine Learning Techniques, then you can opt this course for your carrier.
What will you learn
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Instructions on Data Mining
You will be given step by step instructions on data mining so you can successfully complete all your data science projects without hassle.
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Data Mining in Tableau
At Prolytics, our experts will teach you how to perform the task of data mining in Tableau. This will cover all the basics of Tableau.
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Application of Least Squares method
Our tutors will teach you how to apply the Least Squares method while mining data along with a lowdown on how to create linear regressions.
Once you become a data scientist, you’ll open the door to a highly rewarding career. Below is a list of the skills you’ll learn-
- Tableau to a data or CSV file
- Navigating tableau
- Tableau to an Excel file
- Data mining with Chi Squared
- R-squared
- Linear regression output
- Dummy variables
- Confusion matrix
- Creating derived variables
- Cumulative Accuracy Profile
Lessons
- 217 Lectures
- 23:12:05
- Introduction01:18
- Profession of the future07:02
- Areas of Data Science06:18
- Course Pathways06:22
- Introduction02:18
- Tableau Desktop and Tableau 07:22
- description + view data in file03:24
- Tableau to a Data file,CSV file06:05
- Navigating Tableau - Measures08:54
- Creating a calculated field07:10
- Adding colours08:02
- Adding labels and formatting12:24
- Exporting your worksheet08:44
- Section Recap05:48
- Introduction02:05
- Get Dataset+Project Overview08:12
- Tableau to an Excel File04:18
- visualise an ad-hoc A-B test07:44
- Working with Aliases05:46
- Adding a Reference Line05:36
- Looking for anomalies09:21
- Handy trick validate,approach10:08
- Introduction02:08
- Creating bins & Visualizing10:27
- test for a numeric variable05:04
- Combining two charts09:37
- Data Mining with a Chi-Squared11:45
- there is more than 2 categories09:28
- Estimated Salary distribution12:57
- Chi-Squared Test Stats Tutorial20:24
- Chi-Squared Test Part 210:52
- Section Recap06:44
- Part Completed02:56
- Introduction02:54
- variables:CategoricalvsNumeric06:55
- Types of regressions09:05
- Ordinary Least Squares04:36
- R-squared06:44
- Adjusted R-squared10:27
- Introduction02:45
- Introduction to Gretl03:21
- Get the dataset05:12
- Import data and run descriptive05:54
- Linear Regression Output07:32
- Plotting,analysing the graph05:49
- Introduction02:45
- assumptions of linear regression02:27
- Get the dataset05:21
- Dummy Variables09:26
- Dummy Variable Trap03:28
- BACKWARD, FORWARD, STEPWISE16:51
- Backward,Practice time17:29
- R-squared create Robust models11:37
- Interpreting coefficients,MLR13:21
- Section Recap05:41
- Introduction01:36
- Get the dataset05:47
- Yes/No-Type Business Problems10:38
- Logistic regression intuition18:20
- Your first logistic regression09:48
- False Positives,False Negatives09:57
- Confusion Matrix05:49
- coefficients of a logistic 11:36
- Introduction02:05
- Get the dataset08:41
- geo-demographic segmenation06:52
- model - first iteration09:39
- model - backward elimination12:09
- independent variables11:33
- Creating derived variables07:23
- Checking for multicollinearity09:31
- Matrix,Multicollinearity Intuition09:11
- Model Ready and Section Recap07:55
- Introduction02:44
- Accuracy paradox03:21
- Cumulative Accuracy Profile12:45
- build a CAP curve in Excel15:55
- Assessing your model08:34
- Get my CAP curve template07:47
- test data to prevent overfitting 04:58
- Applying the model to test data09:47
- Comparing training performance12:39
- Section Recap04:41
- Introduction02:05
- Power insights your CAP14:16
- Plan of Attack advanced topic09:44
- Odds Ratio vs Coefficients08:18
- Deriving insights coefficients 14:05
- Section Recap04:25
- Introduction02:44
- model deterioration look like?05:17
- models deteriorate?16:57
- deployed models09:05
- Section Recap02:39
- Introduction02:39
- Data Science project03:54
- Download the dataset section02:44
- Two things before loaded05:18
- Notepad ++02:04
- Editpad Lite02:56
- Introduction02:32
- Download the dataset02:11
- excel can mess up your data04:23
- Bulletproof Blueprint08:18
- Data Wrangling before the Load08:02
- Text qualifier not specified08:19
- your source file is corrupt 119:24
- your source file is corrupt 207:10
- SSIS Error: Data truncation16:47
- finding anomalies in SQL04:58
- Error Handling in SSIS09:33
- Error Handling in SSIS09:08
- How to analyze the error files17:50
- Types of Errors in SSIS05:46
- Summary20:49
- Homework04:05
- Introduction02:03
- Download the dataset01:24
- MS SQL Management Studio03:37
- Shortcut to upload the data05:05
- SELECT * Statement06:19
- WHERE clause to filter data06:18
- Comments in SQL02:39
- How to use Wildcards05:00
- Regular Expressions SQL(%and_)06:18
- Comments in SQL03:41
- Order By06:21
- Data Types in SQL08:48
- Implicit Data Conversion in SQL04:46
- Using Cast() vs Convert()04:41
- Working with NULLs06:38
- LEFT,RIGHT,INNER,OUTER07:52
- Joins with duplicate values03:24
- Joining on multiple fields06:51
- Practicing Joins06:34
- Introduction02:25
- RAW, WRK, DRV tables06:27
- dataset for this section02:27
- first Stored Proc in SQL04:68
- Executing Stored Procedures03:59
- Modifying Stored Procedures09:05
- Create table10:41
- Insert INTO06:29
- tableexists+drop table+Truncate06:54
- Intermediate Recap-Procs05:57
- proc for the second file12:37
- Adding leading zeros08:08
- Converting data on the fly11:24
- How to create a proc template08:32
- Archiving Procs04:12
- LEFT,RIGHT,INNER,OUTER07:52
- Joins with duplicate values03:24
- Joining on multiple fields06:51
- Practicing Joins06:34
- Introduction02:03
- Download dataset this section01:39
- Upload the data to RAW table12:24
- Create Stored Proc06:48
- errors using isnumeric()function08:19
- errors using the len() function08:05
- errors using the isdate() function08:18
- Assurance check: Balance04:32
- Assurance check: ZipCode04:29
- Assurance check: Birthday05:07
- Part Completed10:28
- Introduction02:21
- Cross-departmental Work05:51
- Business Problem03:45
- pre-project communication04:48
- Go and sit with them06:51
- The art of saying "No"06:48
- Sometimes you have go to top03:34
- Building a data culture06:58
- Introduction02:02
- Case study03:28
- Analysing the intro04:19
- Intro dissection - recap10:28
- My brainstorming method04:24
- How to present to executives06:11
- The truth is not always pretty03:43
- Passion and the Wow-factor02:35
- Bonus: my full presentation17:49
Reviews
As a big data specialist, I needed a comprehensive course on data science my skills. Prolytics offered the perfect course that suited both my needs and my budget.
Data mining is a weak point for me but it was fixed completely once I took the data science course at Prolytics. The professional teachers here taught me very well and I’m very happy I took this course.
My dream of becoming a data scientist came true with the Prolytics data science course! This course is very easy on the pocket and extremely detailed. Totally worth it!