DS-Job-ready-with-me

PS: It is a work in progress. Contributions are welcome!🌟

This is a repo created to getting job ready within the field of Data Science/Machine Learning.

---

Background

I am an international student currently studying in Northeastern University pursuing Masters in Applied Machine Intelligence course. I am passionate about data science and have done several projects over the past and even interned at a few organisations.

*This repo shall serve as my personal preparation journey to getting placed. I hope it helps you too.*

Do drop a ⭐ and send a 'Hi' to me over the following socials if you like my work,

1. Linkedin
2. Showwcase
3. Portfolio
4. Medium
5. Youtube
6. Github

Roadmap

This roadmap shall be starting from covering all the fundamental concepts to core concepts. Along the way, we'll be tackling interesting projects in various sectors(health, finance, ecommerce...) and finally solving interview problems. All the links whatever I use to study shall be posted.

Topics covered:

1. SQL & DB Design
2. DSA
3. Probabilty
4. Statistics
5. Machine Learning

*The roadmap is inspired from Ace the Data Science Interview book by Nick Singh and Kevin Huo.*

1. SQL & DB Design

I am following the Coursera's SQL for Data Science to learn SQL.

*For free and thorough SQL end to end course, I suggest Sumit Mittal's SQL playlist on Youtube.*

Track 1:

1. What is SQL?
2. History of data models
3. Relational vs Transactional Databases
4. Creating tables
5. Retrieving data using Select
6. Data modeling and ER diagrams

Track 2:

1. Basics of filtering with SQL
2. Avanced filtering using IN, AND, OR
3. USING WILD CARDS IN SQL ("%, _")
4. Sorting columns using ORDER BY
5. Grouping data using GROUP BY
6. Aggregate functions (MIN, MAX, SUM, COUNT, AVG)

⭐Key Takeaways

Track 3:

1. Subqueries & Joins
2. Cartesian cross joins
3. Inner Join, Outer Join
4. Left outer join, right outer join
5. UNION, INTERSECT

⭐Key Takeaways

Track 4:

1. CONCATENATE(||)
2. TRIM, LTRIM, RTRIM
3. SUBSTR
4. UPPER(UCASE also can be used)
5. LOWER
6. Datetime
7. Views
8. CASE stmts

⭐Key Takeaways

2. DSA

I am studying the DSA following this roadmap suggested by GeeksForGeeks.

I found it very intuitive compared to other resources.

Track 1

1. Time Complexities and Space complexities

   • Practice Questions

2. Asymptotic Analysis

   • Why performance analysis?
   • Is this method feasible?

Track 2

1. Introduction to Arrays
   • Search, insert and delete in an unsorted array
   • Search, insert and delete in a sorted array
   • Moving negative values to one side of an array
   • Merging two sorted arrays
   • Finding specific sub-sequences of integers within the array, such as longest sub-sequence or consercutive subsequence with the largest sum

Problems: [EASY]

1. Contains Duplicate
2. 🤔Two Sum
3. Valid Anagram
4. Valid Palindrome

Problems: [Medium]

1. 🤔Top K frequent elements

[Key Takeaways](Link to the custom readme folder here!)

3. Probability

Loading...

4. Statistics

1. Univariate, bivariate, multivariate analysis
2. Histograms
3. Z Score
4. PDF, CDF

5. Machine Learning

Loading...