Skip to main content

3. TOP 10 Statistics, Python, and SQL concepts for Job Interview

By "Statistics For You"


 TOP 10 Statistics Concepts for Job Interview

1. Sampling and Sample Size Estimation

2. Experiments (A/B tests)

3. Descriptive Statistics (Mean/Median/SD)

4. p-value and Significance Level

5. Probability Distributions

6. Z-test and t-test

7. Conditional Probability and Bayes’ Theorem

8. Correlation

9. Linear Regression

10. Logistic Regression



TOP 10 Python Concepts for Job Interview

1. Reading data from file/table

2. Writing data to file/table

3. Data Types

4. Function

5. Data Processing (Numpy/Pandas)

6. Data Visualisation (Matplotlib/seaborn)

7. Machine Learning (klearn)

8. Deep Learning (Tensorflow/Keras/PyTorch)

9. Distributed Processing (PySpark)

10. Functional and Object-Oriented Programming


TOP 10 SQL Concepts for Job Interview


1. Aggregate Functions (sum/avg/max/min)

2. Group By and Order By

3. JOINs (Inner/Left/Right/Self)

4. Union and Union All

5. Date and Time processing

6. String processing

7. Window Functions (Partition by)

8. Subquery

9. View and Index

10. Common Table Expression (CTE)





 



Comments

  1. It is very useful for me to learn and understand easily. Thanks for sharing your valuable information
    Best Python Training Online
    Python Online Classes

    ReplyDelete

Post a Comment

Popular posts from this blog

UPSC ISS 2020 Toppers Marksheet

Union Public Service Commission  Indian Statistical Service 2020 List of Candidates whose mark sheet is attached in this Blog. 1. NIPUN JAIN  2 JAGRTI GARG  3 LAXMI NARAYAN VERMA  4 CHHAJED HARSHADA NANDALAL   5 PREETAM SEN  6 NAUSHEEN MUSHARRAF  7 SUJIT PADHAN  8 ANCHAL JAIN  9 KIRTI ARORA  10 SUTAPA GHOSH 11 HARSHVARDHAN SINGH GARHWAL 12 KALPNA 13 BHAVNA MISHRA 14 DIVYANSHU MISHRA 15 SHIKHA RAI 16 NAVDEEP KAUR  27 ABHISHEK GAURAV  You can send your mark sheet on our Email: statistics.for.help@gmail.com UPSC ISS 2020 AIR 01 "NIPUN JAIN" UPSC ISS 2020 AIR 02  "JAGRTI GARG"  UPSC ISS 2020 AIR 03  "LAXMI NARAYAN VERMA" UPSC ISS 2020 AIR 04  "CHHAJED HARSHADA NANDALAL"  UPSC ISS 2020 AIR 05  "PREETAM SEN" UPSC ISS 2020 AIR 06  "NAUSHEEN MUSHARRAF" UPSC ISS 2020 AIR 07  "SUJIT PADHAN" UPSC ISS 2020 AIR 08  "ANCHAL JAIN" UPSC ISS 2020 AIR 09  "KIRTI ARORA" UPSC IS...
  2.  10 FEA TURE ENCODING TECHNIQUES EVERY DATA SCIENTIST MUST KNOW FEATURE ENCODING TECHNIQUES  1- LABEL ENCODING    Label encoding is intuitive and easy to understand. Label Encoding refers to converting the labels into the numeric form so as to convert them into the machine-readable form. Machine learning algorithms can then decide in a better way how those labels must be operated. It is an important pre-processing step for the structured dataset in supervised learning.         Example: Suppose we have a column Height in some dataset. After applying label encoding, the Height column is converted into: where 0 is the label for tall, 1 is the label for medium and 2 is the label for short height. Limitation of label Encoding Label encoding converts the data in machine-readable form, but it assigns a unique number (starting from 0) to each class of data. This may lead to the generation of priority issues in the training of data sets. A l...