1 day, 21 hours
5/5

With the Data Science with Python Certification Training course at Zx Academy, you can master the concepts like Sequences and File Operations, Data Manipulation, Supervised Learning, Unsupervised Learning, Dimensionality Reduction, Association Rules Mining, and Recommendation Systems, Model Selection and Boosting, and Time Series Analysis. Accelerate your career with Data Science with Python certification training at Zx Academy.

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Course Overview

The Data Science with Python Certification Training course at Zx Academy teaches the candidate to master Python programming concepts. During the Python Data Science training, a candidate will learn Machine Learning, Data Analysis, Web Scraping, Data Visualization, and NLP. The Python Data Science certification course enables the candidate to learn the concepts from scratch and master vital Python programming concepts like file operations, object-oriented programming, data operations, and many Python libraries like Numpy, Pandas, Matplotlib essential for Data Science. Besides, the Python Data Science course makes the candidate understand various types of Recommendation Systems, Machine Learning, and several concepts of Data Science. Highlights of Zx Academy Training:
  • 24/7 Support throughout the training period
  • Live online classes
  • Training under industry experts
  • Free study material
  • Flexible timings to all our students
What will you learn in Data Science with Python training? After completion of the Data Science with Python training, you will learn:
  • Sequences and File Operations
  • Data Manipulation
  • Supervised Learning
  • Unsupervised Learning
  • Dimensionality Reduction
  • Association Rules Mining and Recommendation Systems
  • Model Selection and Boosting
  • Time Series Analysis
Who should take this Data Science with Python training? The Data Science with Python Certification training course is suited for:
  • Technical Leads, Developers
  • Analytics Managers
  • Business Analysts
  • Predictive Analytics
  • Architects
What are the prerequisites for taking Data Science with Python training? The prerequisites for taking Data Science with Python training are:
  • Basic understanding of computer programming languages.
  • Basics of Data Analysis.
Why should you go for Data Science with Python training? Zx Academy's Data Science with Python course training helps the candidate gain applied data science expertise using Python. This course is packed with many activity assignments, problems, and scenarios that ensure a candidate attains practical experience in addressing predictive modeling problems. The Data Science with Python Certification training covers both fundamental and advanced topics like Python scripts, file operations, and sequence. The candidate can use a few libraries like NumPy, Scikit, Pandas, and Matplotlib. Salary as per market According to Glassdoor, the average salary of a Data Scientist is $1,70,000 per year.

Curriculum – Data Science with Python

  • The Companies using Python
  • Different Applications where it is used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the screen
  • Python files I/O Functions
  • Numbers
  • Strings and related operations
  • Tuples and related operations
  • Lists and related operations
  • Dictionaries and related operations
  • Sets and related operations
  • Functions
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda Functions
  • Object-Oriented Concepts
  • Standard Libraries
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways
  • Errors and Exception Handling
  • Handling Multiple Exceptions
  • NumPy - arrays
  • Operations on arrays
  • Indexing slicing and iterating
  • Reading and writing arrays on files
  • Pandas - data structures & index operations
  • Reading and Writing data from Excel/CSV formats into Pandas
  • matplotlib library
  • Grids, axes, plots
  • Markers, colours, fonts and styling
  • Types of plots - bar graphs, pie charts, histograms
  • Contour plots
  • Basic Functionalities of a data object
  • Merging of Data objects
  • Concatenation of data objects
  • Types of Joins on data objects
  • Exploring a Dataset
  • Analysing a dataset
  • Revision (numpy, Pandas, scikit learn, matplotlib)
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • Gradient descent
  • What are Classification and its use cases?
  • What is Decision Tree?
  • Algorithm for Decision Tree Induction
  • Creating a Perfect Decision Tree
  • Confusion Matrix
  • What is Random Forest?
  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Factor Analysis
  • Scaling dimensional model
  • LDA
  • What is Naïve Bayes?
  • How Naïve Bayes works?
  • Implementing Naïve Bayes Classifier
  • What is Support Vector Machine?
  • Illustrate how Support Vector Machine works?
  • Hyperparameter Optimization
  • Grid Search vs Random Search
  • Implementation of Support Vector Machine for Classification
  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How does K-means algorithm work?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering works?
  • What are Association Rules?
  • Association Rule Parameters
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How does Recommendation Engines work?
  • Collaborative Filtering
  • Content-Based Filtering
  • What is Reinforcement Learning
  • Why Reinforcement Learning
  • Elements of Reinforcement Learning
  • Exploration vs Exploitation dilemma
  • Epsilon Greedy Algorithm
  • Markov Decision Process (MDP)
  • Q values and V values
  • Q – Learning
  • α values
  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • White Noise
  • AR model
  • MA model
  • ARMA model
  • ARIMA model
  • Stationarity
  • ACF & PACF
  • What is Model Selection?
  • The need for Model Selection
  • Cross-Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting
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