Top 5 things I am currently following to boost my learning curve in Data Science as a beginner 🧵👇

#DataScience #Python #100DaysOfCode
1. Trying to implement large projects from start to finish🧑‍💻: Well I am believer of "Learn Best by Doing". As I implement a project from scratch, I do get a lot of errors which ultimately teach me even more.
2. Working with friends 👬: Being a grauduate student, I am very much aware of the importance of working in groups. This method of learning improves my thinking and increases my confidence level.
3. Reading academic papers 📜: I try to find papers which addresses interesting research publication about the topic I study at that time. This provides me more profound knowledge.
4. Maintaining a notebook 🔖: I try to maintain a handwritten notebook which contains all my notes in a neat and clean manner which helps me to understand the topic better and also act as a reference for later use.
5. Regularly watch podcasts 🎙️ on youtube related to data science. Instead of wasting time on useless stuff, I prefer to use it in a productive manner.
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More from @PiyalBanik

19 Jun
Ever wondered how a Data Scientist thinks about a problem? Here are the major steps involved in tackling a data science problem.

Thread 🧵👇

#DataScience #MachineLearning #100DaysOfCode
1. Business Understanding: We should have clarity of what is the exact problem we are going to solve.

What is the problem that we are trying to solve? - Asking the right questions as a Data Scientist starts with understanding the goal of the business.
2. Analytical Approach: How can we use data to answer the question? We should decide the analytical approach to follow which can be of 4 types
- Descriptive
- Statistical
- Predictive
- Prescriptive
and it indicates the necessary data content, formats, and sources to be gathered
Read 12 tweets
18 Jun
Top 7 interesting careers related to Data Science to explore. Pick one and start learning.

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#DataScience #ArtificialIntelligence #MachineLearning #BigData
1. Data Scientist

Data scientist use their analytical and technical capabilities to extract meaningful insight from data.
2. Machine Learning Engineer

Machine Learning engineer's final output is the working software, and their audience for this output consists of other software components that run automatically with minimal human supervision. The decisions are made by machines.
Read 9 tweets
18 Jun
Everything you need to know about Strings in Python for Data Science

Thread 🧵👇

#DataScience #Python #100daysofcodechallenge
📌Looping Through a String

Since strings are arrays, we can loop through the characters in a string, with a for loop. Image
📌String Length
To get the length of a string, use the len() function.

📌Check String
To check if a certain phrase or character is present in a string, we can use the keyword in. Image
Read 8 tweets
17 Jun
Python operators are easy and every aspiring Data Scientist need to know the common ones.

Thread 🧵👇

#Python #DataScience #100DaysOfCode #code #CodeNewbie
📌Python Arithmetic Operators:

Arithmetic operators are used with numeric values to perform common mathematical operations Image
📌Python Assignment Operators:

Assignment operators are used to assign values to variables Image
Read 8 tweets
16 Jun
There are 8 built in classes (variable types) in Python. A thread🧵👇

#Python #100DaysOfCode #CodeNewbie
1. Boolean- The Boolean data type is a truth value, either True​ ​ or Fals​e. Image
2, 3. Integer and Float - An integer is a positive or negative number without floating point. A float is a
positive or negative number with floating point
precision. Image
Read 9 tweets

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