Data Analytics with Python Training

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Course Highlights

  • Instructor-led Online Training
  • Project Based Learning
  • Certified & Experienced Trainers
  • Course Completion Certificate
  • Lifetime e-Learning Access
  • 24x7 After Training Support

Data Analytics with Python Training Course Overview

Multisoft Systems offers a robust Data Analytics with Python training course designed for professionals and enthusiasts eager to delve into the world of data analytics. This course equips learners with the essential skills needed to analyze, interpret, and visualize data effectively using Python, one of the most popular programming languages for data science. The curriculum covers a range of topics including data manipulation with pandas, data visualization with Matplotlib and Seaborn, statistical analysis, and the creation of predictive models using machine learning with scikit-learn. Each module is crafted to provide practical knowledge through hands-on exercises, ensuring that participants can apply their learning in real-world scenarios.

Participants will engage with experienced instructors who bring years of industry knowledge and insights, providing personalized feedback and support throughout the course. The training also includes case studies and projects that mimic actual industry problems, which are invaluable for understanding the challenges and applications of data analytics in business.

By the end of the course, attendees will have a solid foundation in data analytics, along with the Python programming skills necessary to tackle complex data analysis tasks. This makes them valuable assets in various industries such as finance, healthcare, technology, and more, paving the way for a thriving career in data science and analytics.

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Data Analytics with Python Training Course curriculum

Curriculum Designed by Experts

Multisoft Systems offers a robust Data Analytics with Python training course designed for professionals and enthusiasts eager to delve into the world of data analytics. This course equips learners with the essential skills needed to analyze, interpret, and visualize data effectively using Python, one of the most popular programming languages for data science. The curriculum covers a range of topics including data manipulation with pandas, data visualization with Matplotlib and Seaborn, statistical analysis, and the creation of predictive models using machine learning with scikit-learn. Each module is crafted to provide practical knowledge through hands-on exercises, ensuring that participants can apply their learning in real-world scenarios.

Participants will engage with experienced instructors who bring years of industry knowledge and insights, providing personalized feedback and support throughout the course. The training also includes case studies and projects that mimic actual industry problems, which are invaluable for understanding the challenges and applications of data analytics in business.

By the end of the course, attendees will have a solid foundation in data analytics, along with the Python programming skills necessary to tackle complex data analysis tasks. This makes them valuable assets in various industries such as finance, healthcare, technology, and more, paving the way for a thriving career in data science and analytics.

  • Gain a solid understanding of Python programming fundamentals essential for data analysis.
  • Learn to manipulate and prepare data for analysis using pandas, a powerful Python library.
  • Acquire the ability to visualize data effectively using libraries like Matplotlib and Seaborn to uncover insights and trends.
  • Understand the application of statistical methods to derive meaningful information from data.
  • Explore basic machine learning techniques using scikit-learn to predict future trends and behaviors.
  • Engage in hands-on projects that simulate real-world data analytics problems and solutions.
     

Course Prerequisite

  • Basic understanding of programming concepts (any language, though Python is preferred)
  • Fundamental knowledge of statistics and mathematics
  • A keen interest in data analysis and willingness to learn new analytical techniques

Course Target Audience

  • Data Analysts
  • Business Analysts
  • Aspiring Data Scientists
  • IT Professionals
  • Students in technical fields
  • Anyone interested in data-driven decision making

Course Content

  • Define Python
  • Overview of Python
  • Understand why Python is Popular
  • Setup Python Environment
  • Understand Operands and Expressions
  • The Companies using Python
  • Different Applications where Python is Used
  • Discuss Python Scripts on UNIX/Windows
  • Values, Types, Variables
  • Operands and Expressions
  • Conditional Statements
  • Loops
  • Command Line Arguments
  • Writing to the Screen
  • Write your First Python Program
  • Understand Command Line Parameters and Flow Control

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  • Python files I/O Functions
  • Numbers
  • Strings and related operations
  • Tuple - properties, related operations, compared with list
  • List - properties, related operations
  • Dictionary - properties, related operations
  • Set - properties, related operations

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  • Understand Python Standard Libraries
  • Packages and Module - Modules, Import Options, sys Path
  • Functions - Syntax, Arguments, Keyword Arguments, Return Values
  • Function Parameters
  • Global Variables
  • Variable Scope and Returning Values
  • Lambda - Features, Syntax, Options, Compared with the Functions
  • Sorting - Sequences, Dictionaries, Limitations of Sorting
  • Errors and Exceptions - Types of Issues, Remediation
  • Object Oriented Concepts
  • Modules Used in Python
  • The Import Statements
  • Module Search Path
  • Package Installation Ways
  • Errors and Exception Handling
  • Handling Multiple Exceptions

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  • Create arrays using NumPy
  • Perform various operations on arrays and manipulate them
  • Indexing slicing and iterating
  • Read & write data from text/CSV files into arrays and vice-versa
  • Create Series and Data Frames in Pandas
  • Data structures & index operations in pandas
  • Importing and exporting data
  • Indexing and slicing of data structures in pandas
  • Reading and Writing data from Excel/CSV formats into Pandas
  • Create simple plots in matplotlib
  • Grids, axes, plots, Markers, colours, fonts and styling
  • Types of plots - bar graphs, pie charts, histograms, Contour plots
  • Choose the right plot format for a problem at hand judiciously
  • Scale and add style to your plots

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  • 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

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  • Essential Python Revision
  • Necessary Machine Learning Python libraries
  • What is Machine Learning?
  • Machine Learning Use-Cases
  • Machine Learning Process Flow
  • Machine Learning Categories
  • Linear regression
  • List the categories of Machine Learning
  • Illustrate Supervised Learning Algorithms
  • Identify and recognize machine learning algorithms around us

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  • Understand What is Supervised Learning?
  • Define Classification
  • What is 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?
  • Illustrate Logistic Regression & Polynomial Regression

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  • Introduction to Dimensionality
  • Why Dimensionality Reduction
  • PCA
  • Scaling
  • Factor Analysis
  • Scaling dimensional model
  • LDA

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  • 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

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  • What is Clustering & its Use Cases?
  • What is K-means Clustering?
  • How K-means algorithm works?
  • How to do optimal clustering
  • What is C-means Clustering?
  • What is Hierarchical Clustering?
  • How Hierarchical Clustering works?

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  • What are Association Rules?
  • Association Rule Parameters
  • Apriori Algorithm
  • Market Basket Analysis
  • Calculating Association Rule Parameters
  • Recommendation Engines
  • How Recommendation Engines work?
  • Collaborative Filtering
  • Content Based Filtering

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  • 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

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  • What is Time Series Analysis?
  • Importance of TSA
  • Components of TSA
  • Checking Stationarity
  • Converting a non-stationary data to stationary
  • TSA Forecasting
  • White Noise
  • AR model
  • MA model
  • Implementing Dickey Fuller Test
  • Plot ACF and PACF
  • ARMA model
  • ARIMA model
  • Generating the ARIMA plot

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  • What is Model Selection?
  • Need of Model Selection
  • Cross – Validation
  • What is Boosting?
  • How Boosting Algorithms work?
  • Types of Boosting Algorithms
  • Adaptive Boosting
  • Cross Validation
  • AdaBoost

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Data Analytics with Python Training (MCQ) Assessment

This assessment tests understanding of course content through MCQ and short answers, analytical thinking, problem-solving abilities, and effective communication of ideas. Some Multisoft Assessment Features :

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Data Analytics with Python Training FAQ's

Participants will need a computer with internet access and the ability to run Python. Installation guides for Python and related libraries will be provided at the start of the course.

Yes, upon successful completion of the course and all assessments, participants will receive a certification from Multisoft Systems.

While a basic understanding of programming is helpful, beginners who are committed to learning can benefit from this course, provided they meet the other prerequisites.

Support includes access to instructors for queries, discussion forums with other participants, and additional learning resources.

To contact Multisoft Systems you can mail us on info@multisoftsystems.com or can call for course enquiry on this number +91 9810306956

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