10 Steps of Machine Learning 👆

Important Topics to become a data scientist
[Advanced Level]
👇👇

1. Mathematics

Linear Algebra
Analytic Geometry
Matrix
Vector Calculus
Optimization
Regression
Dimensionality Reduction
Density Estimation
Classification

2. Probability

Introduction to Probability
1D Random Variable
The function of One Random Variable
Joint Probability Distribution
Discrete Distribution
Normal Distribution

3. Statistics

Introduction to Statistics
Data Denoscription
Random Samples
Sampling Distribution
Parameter Estimation
Hypotheses Testing
Regression

4. Programming

Python:

Python Basics
List
Set
Tuples
Dictionary
Function
NumPy
Pandas
Matplotlib/Seaborn

R Programming:

R Basics
Vector
List
Data Frame
Matrix
Array
Function
dplyr
ggplot2
Tidyr
Shiny

DataBase:
SQL
MongoDB

Data Structures

Web scraping

Linux

Git

5. Machine Learning

How Model Works
Basic Data Exploration
First ML Model
Model Validation
Underfitting & Overfitting
Random Forest
Handling Missing Values
Handling Categorical Variables
Pipelines
Cross-Validation(R)
XGBoost(Python|R)
Data Leakage

6. Deep Learning

Artificial Neural Network
Convolutional Neural Network
Recurrent Neural Network
TensorFlow
Keras
PyTorch
A Single Neuron
Deep Neural Network
Stochastic Gradient Descent
Overfitting and Underfitting
Dropout Batch Normalization
Binary Classification

7. Feature Engineering

Baseline Model
Categorical Encodings
Feature Generation
Feature Selection

8. Natural Language Processing

Text Classification
Word Vectors

9. Data Visualization Tools

BI (Business Intelligence):
Tableau
Power BI
Qlik View
Qlik Sense

10. Deployment

Microsoft Azure
Heroku
Google Cloud Platform
Flask
Django

Join @datasciencefun to learning important data science and machine learning concepts

ENJOY LEARNING 👍👍

Data Scientist Interview Questions


1. How would you test whether a given dataset follows a normal distribution?
2. Explain the difference between Type I and Type II errors. How do they impact hypothesis testing?
3. You roll two dice. What is the probability that the sum is at least 8?
4. Given a biased coin that lands on heads with probability p, how can you generate a fair coin flip using it?
5. How would you detect and handle outliers in a dataset?
6. How do you deal with an imbalanced dataset in classification problems?
7. Explain how the Gradient Boosting Algorithm works. How is it different from Random Forest?
8. You are given a trained model with poor performance on new data. How would you debug the issue?
9. What is the curse of dimensionality? How do you mitigate its effects?
10. How do you choose the best number of clusters in K-means clustering?
11. Given a table of transactions, write an SQL query to find the top 3 customers with the highest total purchase amount.
12. How would you optimize a slow SQL query that joins multiple large tables?
13. Write an SQL query to calculate the rolling average of sales over the past 7 days.
14. How would you handle NULL values in an SQL dataset when performing aggregations?
15. How would you design a real-time recommendation system for an e-commerce website?

Answering these questions requires an in-depth knowledge of Data Scientist concepts.

5 data science questions you should be able to answer for a data scientist role.

𝐌𝐞𝐝𝐢𝐮𝐦 𝐥𝐞𝐯𝐞𝐥

1. Name ML algorithms that do not use Gradient Descent for optimization.

2. Explain how you construct an ROC-AUC curve.

3. Give examples of business cases where precision is more important than recall, and vice versa.

4. What’s the difference between bagging and boosting, and when would you use one over the other?

5. How do MLE and MAP differ?