An Ultimate Data science starter kit for beginners

Hello Everyone! This is my first blog in my personal website. since I have gotten many request and queries on how to start learning data science , I have decided to write this blog in much more practical way.

This blog will guide you in terms of what are the fundamental concepts to be learnt in the initial stage . If you learn these foundational skills it will be really helpful for you to build advanced skills in data science.

Data Science Starter Kit

Month 1 – Foundation phase

In the month 1 , concentrate more on listed foundational concepts. second month onwards we will start learning intermediate topics such as regression analysis , classification , time series forecasting

Tech – 1 : Python programming language

S.No	Python topic
1	Python variables , statements ,Data types
2	Arithmetic operations , logical and relational operations
3	For , while loops , if conditions
4	User defined functions
5	python data structures strings
6	Object orient programming – Classes and objects
7	Numpy – Matrix operations
8	Pandas – Data frame operations
9	Seaborn,Matplotlib,plotly
10	Scipy , Statsmodels

Tech – 2 : SQL and EXCEL

S.No	Topics to Learn
SQL – 1	Create , insert , alter tables
2	Aggregating, Manipulating and Filtering data
3	Joins and Sub Queries
4	Window functions
5	Unions , Index , Regex
EXCEL -1	Mathematical , Relational ,Logical operations
2	Lookups , Index , match
3	Charts and pivot tables
4	Macros
5	Dashboarding

Math -1 : Statistics and Probability

S.No	Concepts to learn
1	Different types of data and visualizations
2	Mean , Median , Frequency , Mode , Sample and Population
3	Central tendency and Normal Distribution central limit theorem
4	Point estimate and confidence interval , Sampling distribution
5	Null and Alternate hypothesis , Hypothesis testing and distributions
6	T tests , F tests , Z test , ANOVA , Chi Square
7	Correlation and Covariance
8	Non Parametric Hypothesis testing
9	Probability and distributions
10	Different Probability distributions
11	Conditional probability and inverse probability
12	Expectation and Variance
13	Bayes Theorem
14	Maximum likelihood

Business -1 – Understanding applications and case studies of Data science across different domains

Pharmaceuticals

Finance

Banking

Retail

CPG

Telecom

at the end of the first month , you will be having fundamental idea about what is data science , how to handle data , how to visualize data , how to manipulate and get findings from the data. Try to cross learn the concepts and practice the statistical lessons using python or excel. In this stage you will get the skillsets for becoming a data analyst / Business analyst role

Month 2 : Statistical and Machine learning algorithms

Math -1 : Introduction to Statistical learning and Machine learning

S.No	Algorithms to learn
1	Simple linear regression ,Multiple linear regression
2	Penalized linear regression and non linear regression
3	Logistic regression and Genarlised linear models
4	Linear mixed effects models
5	Decision trees , Random forests (Bagging)
6	Gradient boosting , Adaptive Gradient Boosting
7	Extreme Gradient Boosting , Cat Boosting , Light Gradient boosting
8	Stacking and blending
9	K Nearest Neighbors , Naïve Bayes Classification
10	Support vector machine
11	K Means clustering , DB Scan , Hierarchial clustering PCA
12	Time series – ARIMA , AR , MA , SARIMA ,Exponential smoothing
13	Basics of Neural Networks – Weights and Biases
14	Back propagation and Multilayer perceptron
15	Machine learning development lifecycle – MLOPs

Tech stack to be learnt – Month 2

Learn and Practice the following frameworks along with the listed algorithms

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Benchmark Problems to practice :

Practice Regression , classification , clustering , forecasting problems using benchmark datasets

1. Flower species classification – Iris data set
2. Boston House price prediction – Boston dataset
3. Clustering Different wine categories
4. Time series forecasting

Kindly Don’t restrict with the algorithms explore more datasets and practice listed algorithms. learn like a machine ( i mean learn from your errors)

Month 3 : Projects and Portfolio Development

Based on the Learnings from the first 2 months , start building your own projects based on your own design.

Manage your projects in github , use online platforms like kaggle , colab for practicing and working along with the data science community

Start participating in the coding , problem solving competitions in online platforms like leetcode , hackerrank , hackerearth , techgig, analytics vidya,analytics india magazine

Create your portfolio with Various projects from regression , classification , clustering ,forecasting problems .

Prepare a data story flow deck and present them along with your portfolio

start applying for internships or jobs

Never stop learning – continue to learn advanced algorithms and methodologies

Implement the algorithms to serve a purpose / solve a problem in a Retail/Pharma/Banking/CPG/Telecom domain.

This will help you to integrate your business , tech and math skills .

Happy learning – I Hope this blog helps you

Thanks for Reading.