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Data Science With Python Online Training

This Data Science with Python online certification and training course is designed and conceived by leading experts in Python programming. This 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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Course Overview

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.

Key Features 

  • Learning Applied data science with python specialization.
  • Learning concepts of data science python visualization
  • Insight into data science python libraries
  • Data science with python certification guidance
  • Access to real-life scenarios.
  • Help you with Python data science interview questions
Who should take this course?

This 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 Ipython
    • Notebook
    • Python Objects
    • Number & Booleans
    • Strings
    • Container Objects
    • Mutability Of Objects
    • Operators - Arithmetic
    • Bitwise Comparison And Assignment Operators
    • Operators Precedence And Associativity
    • Conditions(If Else, If-Elif-Else)
    • Loops(While For)
    • Break And Continue Statements
    • Range Functions
    • String Object Basics
    • String Methods
    • Splitting And Joining Strings
    • String Format Functions
    • Lists
    • List Comprehensions
    • Tuples
    • Sets
    • Dictionary
    • Functions Basics
    • Iterators
    • Generator Functions
    • Lambda Functions
    • Map
    • Reduce
    • Filter Functions
    • OOPS Basic Concepts
    • Creating Classes And Objects
    • Inheritance
    • Multiple Inheritance
    • Working With Files
    • Reading And Writing Files
    • Buffered Read And Write
    • Other File Methods
    • 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
    • Numpy Variable
    • Numpy Manipulation
    • Arrays
    • Array Indexing
    • Slicing And Iterating
    • Stacking Together Different Arrays
    • View Or Shallow Copy
    • Deep Copy
    • Functions And Methods Overview
    • Broadcasting Rules
    • Fancy Indexing And Index Tricks
    • Indexing With Boolean Arrays
    • The Ix_() Function
    • Indexing With Strings
    • Linear Algebra
    • Simple Array Operations
    • ASSIGNMENT – 1 : PYTHON BASICS
    • Descriptive Analysis
    • Pandas Input-Output
    • Pandas Manipulation
    • Pandas Groupby
    • Importing Libraries - Creating Data Sets - Creating Data Frames - Reading From CSV - Exporting To CSV - Finding Maximums - Plotting Data
    • Reading From TXT - Exporting To TXT - Selecting Top/Bottom Records - Descriptive Statistics - Grouping/Sorting Data
    • Creating Functions - Reading From EXCEL - Exporting To EXCEL - Outliers - Lambda Functions - Slice And Dice Data
    • Adding/Deleting Columns - Index Operations
    • Stack/Unstack/Transpose Functions
    • GroupBy Function
    • Converting Between Different Kinds Of Formats
    • Combining Data From Various Sources
  1. ASSIGNMENT 2 – NUMPY AND PANDAS

    • Linear Algebra (Scipy.Linalg)
    • Statistics (Scipy.Stats) Matplotlib Intro
    • Bar Charts Histogram
    • Scatter Plot
    • Stack Charts
    • Legend Title Style
    • Seaborn
    • Data Cleaning Walkthrough
    • Combining Multiple Datasets To Get A Single And Clean Dataset
    • Reshaping Dataset
    • Sorting And Joins
    • SQL Queries In Pandas
    • Descriptive Statistics
    • Sample Vs Population Statistics
    • Random Variables
    • Probability Distribution Function
    • Expected Value
    • Hypothesis Testing
    • Z-Stats Vs T-Stats
    • Type 1 Type 2 Error
    • Confidence Interval
    • Hypothesis Testing
    • Z-Stats Vs T-Stats
    • Type 1 Type 2 Error
    • Confidence Interval
    • Linear Regression
    • Assumptions
    • R Square Adjusted R Square
    • Hands-On Linear Regression
    • Logistics Regression
    • Precision Recall
    • ROC Curve
    • F-Score
    • Decision Tree
    • Cross Validation
    • Bias Vs Variance
    • ASSIGNMENT 3 – MACHINE LEARNING 1
    • Ensemble Approach
    • Bagging Boosting
    • Random Forest
    • Variable Importance
    • K Nearest Neighbour
    • Lazy Learners
    • Curse Of Dimensionality
    • KNN Issues
    • Text Analytics
    • Tokenizing
    • Chunking
    • Document Term Matrix
    • TFIDF
    • Sentiment Analysis Hands-On
    • Hierarchical Clustering
    • K-Means
    • Performance Measurement
    • Principal Component Analysis
    • Dimensionality Reduction
    • Factor Analysis
    • ASSIGNMENT 4 – MACHINE LEARNING 2
    • Time Series Forecasting
    • Moving Average
    • ARIMA Model
    • Introduction To Deep Learning Basic Of Neural Network
    • Type Of NN
    • Cost Function
    • Gradient Descent A Project With Combination Of All Techniques Covered.
    • Recurrent Neural Network
    • Back Propagation
    • Making Training And Validation Batches
    • Building The Model
    • Hyperparameters
    • Training
    • Sampling
Data Science With Python FAQ’s:
1.Do I get python certification?

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

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.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.

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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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