Data Scientist  Programming Syllabus Complete Road Map Part 2

4 Programming

One needs to have a good grasp of

programming concepts such as Data

structures and Algorithms. The

programming languages used are Python,

R, Java, C++ is also useful in some places

where performance is very important.

  Python:

            ◉  List
            ◉  Set
            ◉  Tuples
            ◉  Dictionary
            ◉  Function, etc.
                   ◎  NumPy
                   ◎  Pandas
                   ◎ Matplotlib/Seaborn, etc.
             ◉  R:
                   ◎  R Basics
             ◉   Vector
             ◉   List
             ◉   Data Frame
             ◉   Matrix
                   ◎  Array
                   ◎  Function, etc.
                   ◎  dplyr
                   ◎  ggplot2
                   ◎  Tidyr
                   ◎  Shiny, etc.
                   ◎  DataBase:
                   ◎  SQL
                   ◎  MongoDB
                   ◎  Web Scraping (Python | R)
                   ◎  Linux
                   ◎  Git                
                   ◎  Other:
                   ◎  Data Structure
                   ◎  Time Complexity

5 Machine Learning 

ML is one of the most vital parts of data

science and the hottest subject of research

among researchers so each year new

advancements are made in this. One at

least needs to understand basic algorithms

of Supervised and Unsupervised Learning.

There are multiple libraries available in

Python and R for implementing these

algorithms.

Machine Learning

 ◉ Introduction:

     ◎ How Model Work

     ◎ Basic Data Exploration

     ◎  First ML Model

     ◎  Model Validation

     ◎  Underfitting & Overfitting

     ◎  Random Forests (Python _| R)

     ◎  scikit-learn

◉  Intermediate:

     ◎ 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

◉  Feature Encoding

◉  Feature Selection

8.  Natural Language Processing

◉  Text Classification

◉  Word Vectors

9.  Data Visualization Tools

◉  Excel VBA

◉  BI (Business Intelligence):

◉  Tableau

◉  Power Bl

◉  Qlik View

◉  Qlik Sense

10. Deployment

The last part is doing the deployment.

Definitely, whether you are fresher or 5+

years of experience, or 10+ years of

experience, deployment is necessary.

Because deployment will definitely give

you a fact is that you worked a lot.

FOR PART 1