Audience & Prerequisites
Schedule & Fees
Python for Data Science Training
ITS Python for Data Science training helps you learn the top programming language for the domain of Data Science. You will master the technique of how Python is deployed for Data Science, work with Pandas library for Data Science, do data munging and data cleaning, advanced numeric analysis and more through real-world hands-on projects and case studies.
- Introduction to Python for Data Science
- OOP concepts, expressions and functions
- What is SQLite in Python, operations and classes
- Creating Pig and Hive UDF in Python
- Deploying Python for MapReduce programming
- Real-world Python for Data Science projects
- Python’s design and libraries provide 10 times productivity compared to C, C++ or Java
- A Senior Python Developer in the United States can earn $102,000 – indeed.com
Python is one of the best programming languages that is used for the domain of Data Science. ITS is offering the definitive Python for Data Science training course for learning Python coding, running it on various systems like Windows, Linux and Mac thus making it one of the highly versatile languages for the domain of Data Analytics. Upon the completion of the training, you will be able to get the best jobs in the Data Science domain for top salaries.
You don’t need any specific knowledge to learn Python. Though, a basic knowledge of programming can help
Course Outline Duration: 4 Days
Introduction to Data Science
What is Data Science, what does a data scientist do, various examples of Data Science in the industries and how Python is deployed for Data Science applications, various steps in Data Science process like data wrangling, data exploration and selecting the model.
Introduction to Python
Introduction to Python programming language, important Python features, how is Python different from other programming languages, Python installation, Anaconda Python distribution for Windows, Linux and Mac, how to run a sample Python script, Python IDE working mechanism, running some Python basic commands, Python variables, data types and keywords.
Hands-on Exercise – Installing Python Anaconda for the Windows, Linux and Mac
Python basic constructs
Introduction to a basic construct in Python, understanding indentation like tabs and spaces, code comments like Pound # character, names and variables, Python built-in data types like containers (list, set, tuple and dict), numeric (float, complex, int), text sequence (string), constants (true, false, ellipsis) and others (classes, instances, modules, exceptions and more), basic operators in Python like logical, bitwise, assignment, comparison and more, slicing and the slice operator, loop and control statements like break, if, for, continue, else, range() and more.
Hands-on Exercise – Write your first Python program, write a Python function (with and without parameters), use Lambda expression, write a class, create a member function and a variable, create an object and write a for loop to print all odd numbers
OOPs in Python
Understanding the OOP paradigm like encapsulation, inheritance, polymorphism and abstraction, what are access modifiers, instances, class members, classes and objects, function parameter and return type functions, Lambda expressions.
Hands-on Exercise – Writing a Python program and incorporating the OOP concepts
NumPy for mathematical computing
Introduction to mathematical computing in Python, what are arrays and matrices, array indexing, array math, Inspecting a numpy array, Numpy array manipulation,
Hands-on Exercise – How to import NumPy module, creating array using ND-array, calculating standard deviation on array of numbers and calculating correlation between two variables.
SciPy for scientific computing
Introduction to SciPy, building on top of NumPy, what are the characteristics of SciPy, various subpackages for SciPy like Signal, Integrate, Fftpack, Cluster, Optimize, Stats and more, Bayes Theorem with SciPy.
Hands-on Exercise: Importing of SciPy, applying the Bayes theorem on the given dataset.
What is a data Manipulation. using Pandas library for data manipulation, NumPy dependency of Pandas library, Series object in pandas, Dataframe in Pandas, loading and handling data with Pandas, how to merge data objects, concatenation and various types of joins on data objects, exploring dataset, Cleaning dataset, Manipulating dataset, Visualizing dataset
Hands-on Exercise – Doing data manipulation with Pandas by handling tabular datasets that includes variable types like float, integer, double and others.
Data visualization with Matplotlib
Introduction to Visualization, Introduction to Matplotlib, Using Matplotlib for plotting graphs and charts like Scatter, Bar, Pie, Line, Histogram and more, Matplotlib API, Subplots and Pandas built-in data visualization.
Hands-on Exercise – Deploying Matplotlib for creating pie, scatter, line and histogram.
Machine Learning using Python
Revision of topics in Python (Pandas, Matplotlib, NumPy, scikit-Learn), Introduction to machine learning, need of Machine learning, types of machine learning, workflow of Machine Learning, Uses Cases in Machine Learning, its various arlogrithms, What is supervised learning, What is Unsupervised Learning,
Hands-on Exercise – Demo on ML algorithms
What is supervised learning, What is linear regression, Step by step calculation of Linear Regression, Linear regression in Python, Logistic Regression, What is classification, Decision Tree, Confusion Matrix, Random Forest, Naïve Bayes classifier (Self paced), Support Vector Machine(self paced), xgboost(self paced)
Hands-on Exercise – Using Python library Scikit-Learn for coming up with Random Forest algorithm to implement supervised learning.
Introduction to unsupervised learning, use cases of unsupervised learning, What is clustering, Types of clustering(self-paced)-Exclusive clustering, Overlapping Clustering, Hierarchical Clustering(self-paced), What is K-means clustering, understanding the K-means clustering algorithm, Step by step calculation of k-means algorithm, Demo on k-means using Scikit , Association Rule Mining(self-paced), Market Basket Analysis(self-paced), Measures in association rule mining(self-paced)-support, confidence, lift, Apriori Algorithm, Demo on Apriori
Hands-on Exercise – Setting up the Jupyter notebook environment, loading of a dataset in Jupyter, algorithms in Scikit-Learn package for performing Machine Learning techniques and training a model to search a grid.
Python integration with Spark-(selfpaced)
Introduction to pyspark, who uses PySpark, need of spark with python, basics of pysark, Pyspark in industry, pySpark installation, pySpark fundamentals, advantage over mapreduce, pySpark Use-cases, and pySpark demo.
Hands-on Exercise: Demonstrating Loops and Conditional Statements, Tuple – related operations, properties, list, etc., list – operations, related properties, set – properties, associated operations, dictionary – operations, related properties.
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