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Data Science With Python Online Training

This course is designed and conceived by leading experts in Python programming. This Python Data Science certification program can help you be a proficient and commanding Python programmer. This online training program is packed with the necessary information and knowledge required for you to be a successful data science Python developer.

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What is Data Science?

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Data science is the study of data in the form of statistics, graphs, mathematical models for data analysis for various needs. Therefore programming for data science with python is the most preferred and efficient. This python data science tutorial will provide you with all the fundamentals of python programming. This program will make you efficient in dealing with data manipulation and cleaning techniques using data science python libraries. Data science without Python would be a difficult thing to crack.

Data Science in Python Course Key Features 

  • Learning concepts of data science python visualization
  • Insight into python data science libraries
  • We Will Provide Course certification guidance
  • Access to real-life scenarios.
  • Help you with interview questions and answers

Who should learn Python for Data Sciences?

This Python Data Science course is will be useful for professionals with a flair for programming. This course will also help beginners and individuals who want to pursue a career in the programming field. This training program will make you learn and understand Python programming from scratch.

Course curriculum
    • Installation of Python And I python
    • What is Notebook and how do you use it in Programming
    • What are Python Objects?
    • What is Boolean number and how do you use Boolean in Python
    • What are Strings in Python?
    • What are Python Container Objects?
    • What is Mutability of Objects?
    • What is Python Arithmetic Operators?
    • What are Bitwise operators and how do you calculate it?
    • What is Assignment Operators in Python?
    • What are Operators Precedence and Associativity in Python?
    • How to use and when to use Conditions(If Else, If-Elif-Else)?
    • How to write While Loop in Python?
    • How to use Break And Continue Statements in Python?
    • What are Python Range Functions?
    • What are String Object Basics?
    • How to use String Methods?
    • Concepts of Splitting And Joining Strings in python
    • What Python String Format method?
    • What are Lists in Python?
    • What are List Comprehensions in Python?
    • What is Tuples and how to use it?
    • What is set in Python and how does it work?
    • What is Python Dictionary?
    • What are the Basic functions in Python?
    • What is Iterators in Python with examples?
    • What are Generator Functions?
    • How to use Lambda Functions?
    • What is Map in Python?
    • What is Reduce in Python?
    • How to use Filter Functions?
    • OOPS Basic Concepts
    • Creating Classes and Objects
    • What is Python Inheritance?
    • What is Multiple Inheritance and its examples?
    • Learn working with Files in Python?
    • Reading and Writing Files in Python?
    • Buffered Read and Write concepts
    • What are Other File Methods in Python?
    • Using Standard Module
    • Creating New Modules
    • Exceptions Handling With Try-Except
    • Reading Delimited Files With The CSV Module
    • Reading JSON Documents
    • Reading XML Documents
    • Reading HTML Documents
    • Creating Tables
    • Inserting And Retrieving Table
    • Updating And Deleting The Data
    • ASSIGNMENT – 1 : PYTHON BASICS
    • How to use NumPy Variable?
    • What is NumPy Manipulation in Python?
    • What is Array and how to use it?
    • What is Array Indexing
    • What is Slicing And Iterating in Python?
    • Stacking Together Different Arrays
    • What is Shallow Copy?
    • What is Deep Copy and how to use it?
    • Functions and Methods Overview
    • What is Broadcasting in Python?
    • Fancy Indexing And Index Tricks in Python?
    • What is Boolean array Indexing?
    • How to use Ix_() Function?
    • Indexing With Strings
    • What is Linear Algebra?
    • How to use Simple Array Operations?
    • Descriptive Analysis
    • What is Pandas Input-Output and how to use it?
    • What are Pandas Manipulation?
    • How to use Pandas Group by
    • Creating Data Sets - Importing Libraries - Creating Data Frames - Reading From CSV - Exporting To CSV - Plotting Data - Finding Maximums
    • Exporting To TXT - Reading From TXT - Descriptive Statistics – Grouping and Sorting Data - Selecting Top to Bottom Records
    • Exporting To EXCEL - Reading from EXCEL - Creating Functions - Outliers - Slice And Dice Data - Lambda Functions           
    • Adding/Deleting Columns - Index Operations
    • Stack/Unstack/Transpose Functions
    • Group by Function
    • Converting Between Different Kinds of Formats
    • Combining Data from Various Sources
    • ASSIGNMENT 2 – NUMPY AND PANDAS
    • Linear Algebra (Scipy.Linalg)
    • Statistics (Scipy.Stats) Matplotlib Intro
    • How to display Bar Charts Histogram?
    • How to use Scatter Plot?
    • What are Stack Charts and how to use it?
    • What is Legend Title Style and how to use it?
    • What is Seaborn used for?
    • Concepts of Data Cleaning Walkthrough in Python?
    • Concepts Combining Multiple Datasets to single dataset and examples
    • How do you Reshape Dataset?
    • How to use Python Sort And Python Join?
    • SQL Queries In Pandas
    • What is Python Descriptive Statistics?
    • Sample Vs Population Statistics
    • What are Random Variables and how to use it?
    • What is Probability Distribution Function?
    • How to find Expected Value in Python?
    • What is Hypothesis Testing?
    • Difference between Z-Stats Vs T-Stats?
    • What are Type 1 and Type 2 Error and the difference between them?
    • How to plot Confidence Interval in Python?
    • Introduction
    • What Supervised and Unsupervised learning in ML
    • What is Semi-Supervised ML?
    • How to implement Reinforcement learning?
    • What is Overfitting and Underfitting in python
    • How is Linear Regression used in Machine Learning?
    • Verifying Assumptions of Linear Regression in Python?
    • R Square Adjusted R Square Overview
    • How do you calculate Hands-On Linear Regression?
    • What is Logistics Regression and how we use it?
    • What is Precision and Recall in ML?
    • How is ROC Curve calculated?
    • What is F-Score and what is good F-Score?
    • What is Decision Tree and Examples of it?
    • What are the types of Cross Validation?
    • Understanding Bias and Variance in ML
    • ASSIGNMENT 3 – MACHINE LEARNING 1
    • What is Ensemble and what are the Ensemble methods in Machine Learning?
    • What is Bagging Boosting technique?
    • What is Random Forest with example?
    • How to determine Variable Importance?
    • What is K Nearest Neighbour used for?
    • What is Lazy Learner technique?
    • What is the Curse of Dimensionality in ML
    • Dealing with KNN Issues
    • What is Text Analytics and how it is used?
    • What is Tokenization and how does it work?
    • What is Chunking method?
    • How is Document Term Matrix used?
    • What is TFIDF in Python and how is it calculated?
    • Steps involved in Sentiment Analysis process?
    • What are the types of Hierarchical Clustering?
    • How do you calculate K-Means?
    • How does Python measure Performance?
    • How do I Use Principal Component Analysis in Python?
    • What is Dimensionality Reduction algorithms?
    • How to do Factor Analysis in Python?
    • ASSIGNMENT 4 – MACHINE LEARNING 2
    • What is Time Series Forecasting with Python?
    • How to find Moving Average?
    • What is ARIMA Model and used?
    • Introduction to Deep Learning Basic Of Neural Network
    • Type of Neural Networks?
    • What is a Cost Function in Neural networks?
    • Concepts of Gradient Descent in Machine Learning
    • What is Recurrent Neural Network Python?
    • How does Back Propagation algorithm work?
    • Making Training and Validation Batches
    • What are the stages of Building the Model in Machine Learning?
    • What are Hyperparameters in ML and how do select them?
    • What is Training in Machine Learning?
    • What are the types of Sampling?
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Data Science With Python FAQ’s:
1.What is data science with Python?

Python is an open-source programming language used for different data science applications. Data science in simple terms is the study of data in various formats and to draw a meaningful analysis.

2.How long does Python certification be valid?

Python institute certificates are valid for a lifetime and need not be renewed.

3.Why you should learn Python Programming?

Python is one of the most widely used programming languages. Python developer is an in-demand job. The job opportunities in this sector are growing evermore.

4.Is Python easy to learn?

Python is easy to learn. It allows writing programs in few lines. Our online training will furthermore make you even more efficient and confident.

5.What are the Job roles in Python programming?

The different job roles in Python are Python Developer, Python architect, Data Scientist, and Technical support.

6.Do you provide Data science python documentation?

Yes, we provide detailed documentation.

7.Do I get Data Science Python Certification?

Yes, We assist and guide you in taking and passing the Python certification. We also provide certification on completion of the course.

8.What is the Range of Data Science Salary?

The Annual pay starts from $60k.

9.Where do you get Python Data Science Handbook PDF?

By joining the FolksIT's online Python data science course, you will get the handbook.

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Schedule your timings according to your convenience. No need to delay your daily work schedule.

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Our learners are provided with real-time industry scenarios and also Industry-specific scenarios for practice and mock tests.

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Why FolksIT

Extreme high-quality interactive training

Cutting-edge curriculum with job-ready training methodologies aligned with industry requirements.

Situational help and work assistance

Top-notch experts bring in the best practices and assignments, with live availability.

Learn & Practice on real-world problems.

Practice on real-world scenarios and data sets with hands-on experience.

A classroom like learning experience

Ultimate learning experience. Engaging, interactive, and communicative.

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