FA-0502Software DevelopmentData & Analytics
Python for data science and ecommerce
Introduction
Why this course
This course has been designed to cater to the needs of learners who are comfortable with python and picking up a specialization of python as an additional concentrated skill. This course will also delve into the various usages of python which and its functions in terms of e-commerce. This course aims to alleviate the unnecessary jargon and cover the parts that are indeed needed.
This course shall also enable the user to have the necessary foundation to take their training to a more advanced level should they wish to do so.
This course is designed for those who are experienced in programming with python. For absolute beginners, a Python beginners course is recommended prior to attempting this course.
Learning outcome:
- Overview of python basics.
- IDEs of python.
- Understand the basic syntax structures.
- Get updated on recent syntax changes in python 3.8 and 3.9 respectively.
- Advanced Data handling in python.
- Working with external libraries in python.
- Working with files and encoding.
- Understand different forms of data acquisition.
- Data Extraction and conversion.
- Learn statistical analytics of data.
- Use Python to get the basics of Data Analytics.
- Use simple scripting to present data.
- Have the ability to visualize data and manipulate them using simple python.
- Understand how data science delves into Machine Learning.
- Machine learning and coding in python using Scikit Learn
- Machine learning and coding in python usingXGBoost
- Using machine learning for predictions and analytics.
- Data Acquisition and scraping.
- Spark
- Applications of machine learning using Spark
Training outline
47 modules
·
01Day 1: Statistics and Probability and tools to use for Data Analytics0 topics
02Python Revision0 topics
03A Crash Course in matplotlib.0 topics
04Advanced Visualization with Seaborn.0 topics
05Covariance and Correlation.0 topics
06Conditional Probability.0 topics
07Bayes' Theorem.0 topics
08Day 2: Real hands on Data Analytics0 topics
09Basic Machine Learning (using Python).0 topics
10Supervised vs. Unsupervised Learning, and Train/Test.0 topics
11Using Train/Test to Prevent Overfitting a Polynomial Regression.0 topics
12Bayesian Methods: Concepts.0 topics
13Practice with:0 topics
14Implementing a Spam Classifier with Naive Bayes.0 topics
15K-Means Clustering.0 topics
16Entropy0 topics
17Decision Trees0 topics
18Assessment,0 topics
19Day 3: Dealing with Real-World Data0 topics
20Bias/Variance Tradeoff0 topics
21K-Fold Cross-Validation to avoid overfitting0 topics
22Data Cleaning and Normalization0 topics
23Cleaning web log data0 topics
24Normalizing numerical data0 topics
25Detecting outliers0 topics
26Feature Engineering 0 topics
27Imputation Techniques for Missing Data0 topics
28Handling Unbalanced Data: Oversampling,0 topics
29Undersampling, and SMOTE0 topics
30Binning, Transforming, Encoding, Scaling, and Shuffling0 topics
31Day 4: Apache Spark: Machine Learning on Big Data0 topics
32Installing Spark0 topics
33Spark Introduction0 topics
34Spark and the Resilient Distributed Dataset (RDD)0 topics
35Introducing MLLib0 topics
36Introduction to Decision Trees in Spark0 topics
37K-Means Clustering in Spark0 topics
38TF / IDF0 topics
39Searching Wikipedia with Spark0 topics
40Using the Spark 2.0 DataFrame API for MLLib0 topics
41Day 5: E-Commerce and ML in the Real World0 topics
42Deploying Models to Real-Time Systems0 topics
43Testing Concepts0 topics
44T-Tests and P-Values0 topics
45Hands-on With T-Tests0 topics
46Determining How Long to Run an Experiment0 topics
47A/B Test Gotchas0 topics
A programme built around your team.
Share your training goals and requirements.