A Complete 20 Week Curriculum to Become a Data Analyst in 2022
Invest 20 weeks to crack one of the most exciting jobs in IT domain…
To get what you want, you have to deserve what you want.” — Tai Lopez.
If you want to read more about data science related stuff like-:
1-Data science Interview Questions
2. Machine Learning Case Studies for domains like Digital Marketing
4. Probability and Statistics Questions
You can follow me here for more…
Introduction
Hi Guys, Today let’s talk about how to start your journey to get a entry in IT domain as a DATA ANALYST. It does not matter which field you belong to, which degree you opt, if you follow this 20 week Curriculum, there is a high probability that you will crack a Data Analyst job comfortably.
To crack any job interview you need to understand the business requirement, you need to be persistent and consistent with your work, you should have a decent portfolio and you should stick with your one plan.
I have seen people changing their paths on the daily basis, by doing that you won’t reach to any destination, you will just move in circles. So make your plan and stick to it.
Before we Begin…
Before we begin, A couple of notes before we dive into it:
I am assuming that you can spend at least 2–3 hours daily or 14–16 hours weekly to prepare for data analyst role
This will not cover everything that you need to know to be a fully equipped data analyst. This will cover what I believe are the most fundamental skills of a data analyst.
This curriculum will not include anything related to deep learning . Deep learning deserves its own 20 weeks on its own — it would be a disservice if I tried to squeeze it in!
Let’s get started…
COURSE STRUCTURE
WEEK-1 TO WEEK-4 (Python Coding Language)
WEEK-5 TO WEEK-7 (Probability and Statistics)
WEEK 8 TO WEEK 10 (SQL/PowerBI)
WEEK 11 TO WEEK 12 (Basic Mathematics)
WEEK 13 TO WEEK 17 (Exploratory Data Analysis)
WEEK 18 TO WEEK 19 (Dimension Reduction Techniques)
WEEK 20 (Basic Machine Learning Algorithms)
WEEK-1 TO WEEK-4(Python Coding Language)
From Week-1 to Week-4, We work on the basics of coding, it’s very important to have the basics of python programming when you start this journey, knowing these topics would help…
- Keywords/Variables
- Control Flow statements
- Simple For Loop/While Loop
- Recursion
- OOP(Object Oriented Programming)
- Multithreading
- File Handling(Exception Handling)
- Numpy and Pandas Library
And lots and lots of practise…
If you have time and energy to learn all this online for free, let me give you some good references…
Python Practice Coding Questions
If you have tried youtube but you did not understand much, you can also join
“CODE WITH ME” Session on DataScienceWala.com
“Code With Me” is an initiative started by me to built a habit of coding, After training more than 2000 students, i have realised most of them lack in coding and that is the reason of rejections at most of the companies.
Also the fee for this is less than your weekend party bill!!!
WEEK-5 TO WEEK-7 (Probability and Statistics)
Now if we are done with the basic coding, it’s important to understand the basic probability and Statistics which is the favourite topic of lot of interviewers.
See as a Data Analyst, you need to deal with lot of data so Statistics can give you tools and resources which can enhance the analysis part.
knowing these topics would help…
- Basics of Probability theory
- Conditional Probability
- Bayes Theorem
- Basics of permutation and combination
- Expectations and Variance
- Descriptive Statistics
- Central Tendency and Deviations
- Plots like Q-Q Plot, Histogram, PDF,CDF,Box Plot, Count Plot, Violin Plot,KS Plot
- Normal Distribution
- Uniform Distribution
- Log Transformation
- Kurtosis
- Box-Cox Transform
- Inferential Statistics
- Hypothesis Testing(Check my blog on this…)
- Confidence Interval
- A/B Testing
Knowing these topics will help you in the interview, I have also written lots of blogs on statistics, if time permits, please check my account.
You can also checkout these amazing YouTube videos,
WEEK 8 TO WEEK 10 (SQL)
Sql is again a very important topic with respect to interviews perspective, Lot of times you will be asked SQL based questions.
Specially for data analyst job roles, SQL is again a favourite topic.
knowing these topics would help…
- Difference Between SQL AND MYSQL
- DataBase Management Systems
- Order of Keywords
- JOINS
- Wildcard Operators
- Basic Queries involving GroupBy, Having.
- Inner Queriers
- Practise One or More PowerBI dashboards
let me give you some good references…
WEEK 11 TO WEEK 12 (Basic Mathematics)
Maths is topic which will give you an edge in the interviews, let me share the topics you can prepare.
- Vectors( Topics like Angle between 2 vectors, Projection of one vector onto another vector, Dot Product)
- Matrices(Addition, Multiplication of matrix)
- Linear Algebra(Topics like Euclidian distance, distance of a point to a line, equation of plane, equation of hyperplane, distance of a point to a hyperplane)
- Differentiation(Basics of differentiation, chain rule, Addition of derivative, Division of derivative)
The best source to learn basic maths is KHAN ACADEMY.
WEEK 13 TO WEEK 17 (Exploratory Data Analysis)
In EDA, you can download any data from Kaggle, and start doing data analysis. You can keep few topics in mind while doing data analysis.
- Scaling of features/Columns, Types of Scaling.(Check my blog on this…)
- Converting Categorical Features to Numerical Features(ONE HOT ENCODING)
- Also see libraries like Numpy and Pandas, see their functionalities.
- UNIVARIATE ANALYSIS(Histogram, Pdf, Cdf, Line plot, Bar plot, Count plot,Box Plot, Violin Plot)
- Bivariate Analysis(Scatter Plot, Pair plot)
Some frequently asked questions in this area…
- How to handle missing values in data?
- How to handle imbalance data?
- How to handle lot of dimensions/features/columns?
- What is the difference between classification and regression?
- What is supervised and unsupervised learning?
WEEK 18 TO WEEK 19 (Dimension Reduction Techniques)
This is a bit advance topic but will help you in understanding the data world in much more depth.
Generally asked questions are…
- What is curse of dimensionality?
- Why do we have to reduce the dimension in the data?
- What are the techniques to reduce the dimensions in data?
- What is PCA?
- What is T-SNE?
- How time complexity is related to dimensionality?
WEEK 20 (Basic Machine Learning Algorithms)
Again Learning the basic algorithms wont be a bad option, let me give you some questions which you can prepare.
- What is KNN?
- Difference between KNN and K-Means?(a very famous questions, don’t know the reason though)
- How Naive Bayes works?
- What is the assumption of Naive Bayes algorithm?
- What is Logistic Regression?
- What is sigmoid function?
- What are the failure cases of KNN?
- Can logistic regression handle outliers?
Machine Learning algorithm resources
So this was WEEK BY WEEK schedule for 20 weeks, This blog obviously can’t claim that it will cover all the data analyst interviews but this blog covers almost 80–90% of the topics which can be asked in Data Analyst Interviews.
Portfolio Building
It is very important to build a good portfolio, some of the suggestions are…
- Have a good and updated LinkedIn profile
- Have an updated Resume(You can use ZETY to make a good resume)
- You can use portals like Indeed.com or Naukri.com to search jobs.
- Start writing technical blogs on medium, This shows your passion and consistency for the subject.
- Have an updated Github profile.
Cracking an interview is a skill, I wish you all the best for your interviews.
Thank you for reading….
