Statistics For You

SQL CHEAT SHEET (5 Minutes Read of Structured Query Language (SQL)) SQL (Structured Query Language) is a standardised programming language used to manage relational databases and execute various operations on related data. SQL, which was developed in the 1970s, is now widely used not only by database administrators but also by developers building data integration scripts and data analysts wanting to set up and perform analytical queries.  SQL is used to modify database table and index structures, add, update, and delete rows of data, and retrieve subsets of information from inside a database for transaction processing and analytics applications. Queries and other SQL operations take the form of statements, which are regularly used instructions. Select, add, insert, update, delete, create, change, and truncate are all SQL statements. In this blog, we will learn how to perform basic operations in SQL. Get function for inserting data, update data, deleting data, grouping data, order data,

What is Probabilistic Programming? Probabilistic Programming(PP) is also a relatively unknown topic for many data scientists. However, this is an area that quickly becomes more important. PP is a paradigm of programming that specifies probabilistic patterns and automatically deducts these patterns. It aims to bring together probabilistic modelling and traditional programming of the general-purpose to enhance and facilitate the latter. It can be used to build systems that assist decision-making in the presence of difficulty. In this blog, I briefly discuss the specific situation of the PP. PP can be considered a tool for statistical modelling.  Randomization is the essential element of the PP and the objective of the PP is to give a statistical analysis that explains a phenomenon. Probabilistic programming language (PPL) is built on some elements, that; we have a number of fundamental elements to generate random numbers, primitive ones to estimate probabilities & expectations and ul

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 ISS 2020 AIR 10 "SUTAPA GHOSH"

3. TOP 10 Statistics, Python, and SQL c oncepts 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. S

  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 label with a high value may be considered to have