Data Science Lessons

 I worked on Regresission problems for three weeks. I read concepts and learned how to implement linear regression in python by studying Machine learning using a python book from Dinesh Kumar published by Wiley India Pvt Ltd. I built projects and multiple regression models to understand concepts, here in this blog I am sharing all concepts I learned:- Table of contents: Regression fundamentals and concepts The regression equation and coefficients (image) Python implementation (image) Metrics and errors (Image) Optimization and regulation (image) Other points to keep in mind 1) Regression fundamentals and concept > Regression is widely used in business applications such as sales prediction, house price prediction or cost of the patient in a hospital prediction, etc > regression models fit the best line to inputs(X) to predict the output (Y) 2) Regression equation and coefficients > Linear regression model follows very simple equation as shown below ...

Python Programming language overview: Python is increasingly becoming a popular programming language as a general programming language as well as applications such as data science, machine learning, and artificial intelligence. Python has gained immense popularity among developers and has ranked 1st on preference by developers across the world. A major reason for its popularity could be the ease of reading and writing syntaxes, English like syntaxes make the programming highly interpretable and implementable. Python's philosophy has been the guiding force for its development and usage  One of the biggest advantages python offers is that it has a vast number of standard modules and libraries that can be used to write clean, efficient, and readable code to deliver and deploy in real-world scenarios faster Contains: 1) Python syntaxes 2) Data types and values 3) Conditionals statements 4) operators 5) Loops 6) Functions 7) Data structures 8) Classes and objects 9) String objects ...

    In any data science task after preparing data and understanding data, data scientists want to understand what are features/attributes are there in the data to be extracted. how many categorical variables are there and how many numerical variables are there in the dataset. today in this blog we will only talk about numerical data only. as we want to understand how statistical methods help us to summarise and understand data better.   I will focus on what are various statistical techniques are there and when to apply them to get a particular outcome in a given dataset. here are the topics to be covered: 1) Summary statistics 2) Sampling methods 3) Hypothesis  testing 4) Estimation statistics 1) Summary statistics:    They're some of the very basic methods are there in summary statistics to summarise given data distribution. like 1) Mean 2) Median 3) Mode 4) Standard deviation 5) Variance 6) Range 7) percentiles 8) Interquarantile range 9) Min/Max values...

  Hello and welcome to the data science blog site. today I am going to talk about some other sides of the data science field which you might have been aware of or not, that is nothing but 'the arts'. yeah! you heard it right artistic skills are really important to present your data science solutions that you have figured out from data modeling or crunching your data from various sources etc. if you can't present and tell your story to your audience then your solution has no meaning at all. you have to sell your story effectively and visually compellingly way, and that's where data visualization comes into the picture. Here's what we are going to cover: 1) Ideas on visualizations 2) Storytelling 3) Visual display of data 1) Ideas on visualizations: What is data visualizations:   data visualization is nothing but visualizing structured, raw, and numerical data in various forms of charts and graphs to let your audience understand data. that's no big deal, a simple ...

  Hello and welcome to the Data science lessons blog. to perform any data science task mathematics knowledge and its application will be really important. in fact, it's inevitable in the data science field. Mathematics can be divided into four parts for the Data Science field: 1) Statistics (Descriptive and Inferential): 2) Linear Algebra 3) Probability 4) Optimization  1) Statistics: I cannot imagine data science without this evergreen field of Statistics and its applications across the industries and research fields. basically, statistical methods help us to summerise quantitative data and to get insights out of it. it is not easy to gain any insights by just seeing raw numerical data in any way, until and unless you are a math genius! Topics about Descriptive Statistics: 1) Mean, Median, Mode 2) IQR, percentiles 3) Std deviation and Variance 4) Normal Distribution 5) Z-statistics and T-statistics 6) correlation and linear regression Topics about Inferential Statistics: 1) S...

    Hello and welcome to the data science lessons blog page. I really wish that you find this blog insightful and informative in your journey. as I am writing this blog, I am a beginner in the data science and AI field. I writing this in keeping in mind the future audience of this blog and readers like you. here are the topics we will cover in this introduction blog post. 1) Introduction to myself and purpose of writing this blog 2) what you'll find on this blog site? 3) what I am learning on this blogging journey? 4) Topics to learn to become a data scientist 1) Introduction to myself and purpose of writing this blog:    Hi, I am Avikumar Talaviya from Gujarat state in India. I used to work as a freelance business consultant in the solar energy field and worked with b2b clients in India. Currently, I am pursuing my Bachelor's in Data Science and Analytics field from JAIN University, Banglore, Karnataka. I am keenly passionate about technology and the digital ...