Introduction to Data Science with Python
1. What is Data Science, and how does it work?
2. Why should you choose Python for data science?
3. Relevance in the workplace and the need of the hour
4. How are prominent firms using Python to harness the power of data science?
5. The many stages of a typical Analytics/Data Science project, as well as the involvement of Python
Python Overview – Getting Started with Python
1. Introduction to Python installation
2. Introduction to Python Editors and IDEs (Canopy, PyCharm, Jupyter, Rodeo, Ipython, and so on...).
3. Learn how to use the Jupyter notebook.
4. Important packages (NumPy, SciPy, scikit-learn, Pandas, Matplotlib, and so on) are represented by the concept of packages/libraries.
5. Packages and Name Spaces: Installing and Loading
6. Types of data and data objects/structures (strings, Tuples, Lists, Dictionaries)
7. Comprehensions from lists and dictionaries
8. Date & Time Values – Variable & Value Labels
9. Mathematical – string – date – basic operations
10. Data reading and writing
11. Conditional statements and control flow
Using Python Modules to Access, Import, and Export Data
1. Data from a variety of sources is imported (Csv, txt, excel, access etc)
2. Input into the database (Connecting to database)
3. Data can be exported in a variety of formats.
4. Pandas is an important Python module.
Data Manipulation – cleansing – Munging using Python modules
1. Python for Data Cleansing
2. Sorting, filtering, duplicates, merging, appending, subsetting, derived variables, sampling, data type conversions, renaming, formatting, and other data manipulation steps
3. Built-in Functions in Python (Text, numeric, date, utility functions)
4. User Defined Functions in Python
5. Getting rid of the superfluous data
6. Data Normalization & Data Formatting
7. Python data manipulation modules (Pandas, Numpy, re, math, string, date time, etc.)
Python for Data Analysis and Visualization
1. Exploratory data analysis introduction
2. Frequency tables, descriptive statistics, and summaries
3. Analysis of a Single Variable (Distribution of data & Graphical Analysis)
4. Analysis of Bivariate Data (Cross Tabs, Distributions & Relationships, Graphical Analysis)
Basic statistics and python implementation of statistics methods
1. Measures of Central Tendencies and Variance in Basic Statistics
2. Theorem of the Central Limit – Probability Distributions – Normal Distribution
3. Hypothesis Testing – Concept of Inferential Statistics – Sampling
4. Z/t-tests (one sample, independent, paired), Anova, Correlation, and Chi-square tests are some of the statistical methods used.
Python Implementation of Machine Learning Algorithms & Applications
1. Cluster Analysis – Segmentation (K-Means)
2. CART/CD 5.0 Decision Trees
3. Learning in a group (Random Forest, Bagging & boosting)
4. Artificial Neural Networks are a type of artificial neural network (ANN)
5. Vector Support Machines (SVMs) (SVM)
6. KNN, Nave Bayes, and PCA are examples of other techniques.
7. Text Mining with NLTK (Introduction)
8. Time Series Forecasting: An Overview (Decomposition & ARIMA)
9. Linear and Logistic Regression are two types of regression models.
10.Using K for clustering
Project – Consolidate Learnings
1. Using many algorithms to tackle the problem
2. Benchmarking and issues
3. The outcomes
Modules for Machine Learning Training
Each module has all of the necessary tools for teaching machine learning techniques in Python and R, the two most prominent programming languages. You will learn how to use R and Python to create a data gateway between databases and how to connect to external data sources. Machine Learning is a key component of Data Science. Using the two most prominent technologies, R and Python, learn about the numerous modules that make up Machine Learning. As a data scientist, you will work with a variety of data mining techniques, both supervised and unsupervised. Reinforcement learning, which allows machines to learn through rewards, is one of the most common variations of the same.
Python and R Machine Learning Primer
Machine Learning is a key component of Data Science. Using the two most prominent technologies, R and Python, learn about the numerous modules that make up Machine Learning. With the help of R and Python, learn a general overview of machine learning and the many quality indicators.
Dealing with Imbalanced vs Balanced Datasets
In the actual world, datasets are frequently unable to be used often times, necessitating some kind of preparation. Imbalance in the output classes is a prevalent issue, with proportions as skewed as 95 percent to 5% or even more. Learn about the various strategies and procedures for dealing with data sets that are unbalanced.
Supervised, unsupervised, and reinforcement learning data mining
As a data scientist, you will work with a variety of data mining techniques, both supervised and unsupervised. Reinforcement learning, which allows machines to learn through rewards, is one of the most common variations of the same. Learn about all of the supervised approaches for prediction and classification in this subject, as well as the major unsupervised learning methods and the use of reinforcement learning in Data Mining.
Linear and Logistic Regression using Python and R
Under supervised learning, linear regression is one of the most popular approaches for predicting numeric data, and logistic regression is one of the most popular methods for categorizing categorical data. This module will go through both of these strategies in depth. In addition, the participant will be exposed to a variety of R and Python-based examples.
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