Kerjaya Data Scientist

Perjalanan Kerjaya Data Scientist

Kemahiran Asas

1. Python

2. Object Oriented Programming

Data Engineering

1. Jupyter Notebook

2. numpy

3. pandas

4. matplotlib

Machine Learning

1. Supervised

2. Unsupervised

Cadangan Kursus Data Scientist

Pre-class Preparation

Class 1: Introduction to programming and Python

1. Introduction to programming
2. Python uses
3. Python properties
4. Python syntax
5. Python data types
6. Python operators

Class 2: Learn string data type, and define your own functions

1. String data type
2. Iterate string characters
3. String indexing, slicing
4. String methods
5. Idea of immutable / mutable
6. Define functions
7. How to pass arguments

Class 3: More Python Types & Functions

1. Learn more data types, and define your own functions
2. Collection data types in python: tuple, list, set, dict
3. Deepen understanding of mutable / immutable
4. Shallow copy / deep copy
5. In operator
6. Recursion function

Class 4: Python for Data Analytics

1. Installing packages: jupyter, numpy, scipy, matplotlib
2. Learn markdown syntax: title, paragraph, table, code, link, graph, math formula, etc.
3. Jupyter notebook: how to start, how to edit cells

Class 5: Learn numpy & matplotlib

1. Create code cells to test numpy packages, learn create array, access array items, array operation.
2. Understand matplotlib chart structures and objects
3. Create code cells to try matplotlib and plot charts

Class 6: Pandas

1. Series in pandas.
2. DataFrame in pandas.
3. Data manipulation with DataFrame, merge, pivot_table, etc.
4. Cross connect the DataFrame function with microsoft excel functions, and database join statements.
5. Create jupyter notebook files, explore and practice.

Class 7: Data Engineering

1. CSV format files and using excel to view csv files.
2. Understand how pandas can help to read/produce csv files.
3. Practice with the real dataset;
4. Pandas reading file into dataframe.
5. Dataframe manipulation: groupby, agg, selection, count, etc.
6. Merging 2 dataframes.
7. Plotting with matplotlib

Class 8: Supervised Machine Learning

1. Using SciPy Stats package for Machine Learning
   • Know basic structure and facts about SciPy stats package
   • Know how to use SciPy stats package to help analyze data.
2. SciPy Statistics & Linear Regression
3. Practice with a dataset, learn using norm distribution.
4. Practice with the football dataset, learn Poisson distribution.
5. Learn the procedure of linear regression with continuous RV, using SciPy, Scikit-Learn;
   • SciPy stats normal distribution, how to fit, how to check results.
   • Poisson discrete distribution, how to fit and how to check correlation.
   • Linear regression with WHO selected dataset.

Class 9: Unsupervised Machine Learning

1. Logistic Regression & Clustering
2. Learn what is logistic regression and how to use scikit-learn and statsmodels to do it
3. Learn what is clustering work in machine learning and how to use scikit-learn and scipy stats to do it
4. K-means and Hierarchical clustering of scikit-learn and SciPy stats