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Data Science with Python Course Overview


The Python Data Science course teaches you to master the concepts of Python programming. Through this Data Science with Python certification training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential tools of Data Science with Python.

Python for Data Science certification course enables you to master Data Science using Python. You will work on various Python libraries such as SciPy, NumPy, Matplotlib, Lambda function, etc. You will master Data Science Analytics skills through real-world projects covering multiple domains such as retail, e-commerce, finance, etc

 

Data Science With Python Key Features

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  • 68 hours of blended learning
  • 4 industry-based projects
  • Interactive learning with Jupyter notebooks 
  • Lifetime access to self-paced learning
  • Dedicated mentoring session 
  • In-blended depth learning

 

Skills Covered

  • Data wrangling
  • Data exploration
  • Data visualization
  • Mathematical computing
  • Web scraping
  • Hypothesis building
  • Python programming concepts
  • NumPy and SciPy package
  • ScikitLearn package for Natural Language Processing

 

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Benefits

Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. The demand for Data Science professionals will grow with an estimated 137,630 job openings in India by 2025  and professionals with Python skills will have an additional advantage.

 

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REACH OUT TO US FOR MORE INFORMATION


+91 844 844 0724

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Training Options

Explore all of our training options and pick your suitable ones to enroll and start learning with us! We ensure that you will never regret it!

Self-Paced Learning
549
  • Lifetime access to high-quality self-paced eLearning content curated by industry experts
  • 4 hands-on projects to perfect the skills learnt
  • 3 simulation test papers for self-assessment
  • Lab access to practice live during sessions
  • 24x7 learner assistance and support
Online Bootcamp Training
599
  • Everything in Self-Paced Learning, plus
  • 90 days of flexible access to online classes
  • Live, online classroom training by top instructors and practitioners

 

Classes starting from:-

  11th Mar 2024:Weekday Class

  16th Mar 2024:Weekend Class

Corporate Training
Customized to your learning needs
  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Data Science with Python Course Curriculum

Eligibility

The demand for Data Science with Python programming professionals has surged, making this course well-suited for participants at all levels of experience. This Data Science with Python course is beneficial for analytics professionals willing to work with Python, Software, and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.

Pre-requisites

To best understand the Python Data Science course, it is recommended that you begin with the courses including, Introduction to Data Science in Python, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this training.

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Course Content

Live Course

Self Paced

  • 1.01 - 0.001 Course Overview
    04:34
  • 2.01 - 1.001 Introduction to Data Science
    08:08
  • 2.02 - 1.002 Different Sectors Using Data Science
    05:44
  • 2.03 - 1.003 Purpose and Components of Python
    05:44
  • 2.04 - 1.4 Quiz
    02:32
  • 2.05 - 1.005 Key Takeaways
    02:32
  • 3.01 - 2.001 Data Analytics Process
    07:28
  • 3.02 - 2.2 Knowledge Check
    02:32
  • 3.03 - 2.3 Exploratory Data Analysis(EDA)
    02:32
  • 3.04 - 2.4 EDA-Quantitative Technique
    02:32
  • 3.05 - 2.005 EDA - Graphical Technique
    05:44
  • 3.06 - 2.006 Data Analytics Conclusion or Predictions
    06:07
  • 3.07 - 2.007 Data Analytics Communication
    05:44
  • 3.08 - 2.8 Data Types for Plotting
    01:21
  • 3.09 - 2.009 Data Types and Plotting
    01:21
  • 3.10 - 2.10 Knowledge Check
    02:32
  • 3.11 - 2.11 Quiz
    01:21
  • 3.12 - 2.012 Key Takeaways
    02:32
  • 4.01 - 3.001 Introduction to Statistics
    01:21
  • 4.02 - 3.2 Statistical and Non-statistical Analysis
    02:32
  • 4.03 - 3.003 Major Categories of Statistics
    01:21
  • 4.04 - 3.4 Statistical Analysis Considerations
    02:32
  • 4.05 - 3.005 Population and Sample
    00:25
  • 4.06 - 3.6 Statistical Analysis Process
    01:21
  • 4.07 - 3.007 Data Distribution
    01:21
  • 4.08 - 3.8 Dispersion
    01:21
  • 4.09 - 3.9 Knowledge Check
    02:32
  • 4.10 - 3.010 Histogram
    01:21
  • 4.11 - 3.11 Knowledge Check
    01:21
  • 4.12 - 3.012 Testing
    9:00
  • 4.13 - 3.13 Knowledge Check
    02:32
  • 4.14 - 3.014 Correlation and Inferential Statistics
    02:32
  • 4.15 - 3.15 Quiz
    02:32
  • 4.16 - 3.016 Key Takeaways
    05:44
  • 5.01 - 4.001 Anaconda
    02:32
  • 5.02 - 4.2 Installation of Anaconda Python Distribution (contd.)
    00:25
  • 5.03 - 4.003 Data Types with Python
    00:54
  • 5.04 - 4.004 Basic Operators and Functions
    02:32
  • 5.05 - 4.5 Quiz
    07:28
  • 5.06 - 4.006 Key Takeaways
    02:32
  • 6.01 - 5.001 Introduction to Numpy
    02:45
  • 6.02 - 5.2 Activity-Sequence it Right
    00:54
  • 6.03 - 5.003 Demo 01-Creating and Printing an ndarray
    05:25
  • 6.04 - 5.4 Knowledge Check
    01:21
  • 6.05 - 5.5 Class and Attributes of ndarray
    02:32
  • 6.06 - 5.006 Basic Operations
    05:44
  • 6.07 - 5.7 Activity-Slice It
    02:32
  • 6.08 - 5.8 Copy and Views
    01:21
  • 6.09 - 5.009 Mathematical Functions of Numpy
    05:44
  • 6.10 - 5.010 Analyse GDP of Countries
    01:21
  • 6.11 - 5.011 Assignment 01 Demo
    07:28
  • 6.12 - 5.012 Analyse London Olympics Dataset
    02:32
  • 6.13 - 5.013 Assignment 02 Demo
    07:28
  • 6.14 - 5.14 Quiz
    01:21
  • 6.15 - 5.015 Key Takeaways
    01:21
  • 7.01 - 6.001 Introduction to SciPy
    05:44
  • 7.02 - 6.002 SciPy Sub Package - Integration and Optimization
    07:28
  • 7.03 - 6.3 Knowledge Check
    01:21
  • 7.04 - 6.4 SciPy sub package
    01:21
  • 7.05 - 6.005 Demo - Calculate Eigenvalues and Eigenvector
    02:32
  • 7.06 - 6.6 Knowledge Check
    01:21
  • 7.07 - 6.007 SciPy Sub Package - Statistics, Weave and IO
    05:44
  • 7.08 - 6.008 Solving Linear Algebra problem using SciPy
    02:32
  • 7.09 - 6.009 Assignment 01 Demo
    07:28
  • 7.10 - 6.010 Perform CDF and PDF using Scipy
    06:07
  • 7.11 - 6.011 Assignment 02 Demo
    02:32
  • 7.12 - 6.12 Quiz
    01:21
  • 7.13 - 6.013 Key Takeaways
    05:44
  • 8.01 - 7.001 Introduction to Pandas
    12:00
  • 8.02 - 7.2 Knowledge Check
    02:32
  • 8.03 - 7.003 Understanding DataFrame
    01:21
  • 8.04 - 7.004 View and Select Data Demo
    02:32
  • 8.05 - 7.005 Missing Values
    07:28
  • 8.06 - 7.006 Data Operations
    02:35
  • 8.07 - 7.7 Knowledge Check
    9:00
  • 8.08 - 7.008 File Read and Write Support
    02:32
  • 8.09 - 7.9 Knowledge Check-Sequence it Right
    02:32
  • 8.10 - 7.010 Pandas Sql Operation
    05:44
  • 8.11 - 7.011 Analyse the Federal Aviation Authority Dataset using Pandas
    02:32
  • 8.12 - 7.012 Assignment 01 Demo
    01:21
  • 8.13 - 7.013 Analyse NewYork city fire department Dataset
    02:32
  • 8.14 - 7.014 Assignment 02 Demo
    01:21
  • 8.15 - 7.15 Quiz
    02:32
  • 8.16 - 7.016 Key Takeaways
    02:32
  • 9.01 - 8.001 Machine Learning Approach
    07:28
  • 9.02 - 8.002 Steps One and Two
    01:21
  • 9.03 - 8.3 Steps Three and Four
    02:32
  • 9.04 - 8.004 How it Works
    05:44
  • 9.05 - 8.005 Steps Five and Six
    02:32
  • 9.06 - 8.006 Supervised Learning Model Considerations
    06:07
  • 9.07 - 8.008 ScikitLearn
    02:32
  • 9.08 - 8.010 Supervised Learning Models - Linear Regression
    11:10
  • 9.09 - 8.011 Supervised Learning Models - Logistic Regression
    12:00
  • 9.10 - 8.012 Unsupervised Learning Models
    11:00
  • 9.11 - 8.013 Pipeline
    05:44
  • 9.12 - 8.014 Model Persistence and Evaluation
    02:45
  • 9.13 - 8.15 Knowledge Check
    08:08
  • 9.14 - 8.016 Analysing Ad Budgets for different media channels
    02:32
  • 9.15 - 8.017 Assignment One
    07:28
  • 9.16 - 8.018 Building a model to predict Diabetes
    05:44
  • 9.17 - 8.019 Assignment Two
    05:44
  • 9.18 - Knowledge Check
    04
  • 9.19 - 8.021 Key Takeaways
    02:45
  • 10.01 - 9.001 NLP Overview
    07:28
  • 10.02 - 9.2 NLP Applications
    01:12
  • 10.03 - 9.3 Knowledge Check
    03:32
  • 10.04 - 9.004 NLP Libraries-Scikit
    01:12
  • 10.05 - 9.5 Extraction Considerations
    02:11
  • 10.06 - 9.006 Scikit Learn-Model Training and Grid Search
    04:34
  • 10.07 - 9.007 Analysing Spam Collection Data
    01:21
  • 10.08 - 9.008 Demo Assignment 01
    09:0`
  • 10.09 - 9.009 Sentiment Analysis using NLP
    01:21
  • 10.10 - 9.010 Demo Assignment 02
    08:08
  • 10.11 - 9.11 Quiz
    01:21
  • 10.12 - 9.012 Key Takeaways
    02:32
  • 11.01 - 10.001 Introduction to Data Visualization
    12:00
  • 11.02 - 10.2 Knowledge Check
    01:12
  • 11.03 - 10.3 Line Properties
    03:32
  • 11.04 - 10.004 (x,y) Plot and Subplots
    04:34
  • 11.05 - 10.5 Knowledge Check
    03:32
  • 11.06 - 10.006 Types of Plots
    9:00
  • 11.07 - 10.007 Draw a pair plot using seaborn library
    01:21
  • 11.08 - 10.008 Assignment 01 Demo
    02:11
  • 11.09 - 10.11 Quiz
    01:12
  • 11.10 - 10.012 Key Takeaways
    03:32
  • 12.01 - 11.001 Web Scraping and Parsing
    07:28
  • 12.02 - 11.2 Knowledge Check
    01:21
  • 12.03 - 11.003 Understanding and Searching the Tree
    05:44
  • 12.04 - 11.4 Navigating options
    03:32
  • 12.05 - 11.005 Demo3 Navigating a Tree
    04:34
  • 12.06 - 11.6 Knowledge Check
    01:21
  • 12.07 - 11.007 Modifying the Tree
    02:45
  • 12.08 - 11.008 Parsing and Printing the Document
    05:44
  • 12.09 - 11.009 Web Scraping of Fiest Tech Website
    07:28
  • 12.10 - 11.010 Assignment 01 Demo
    02:35
  • 12.11 - 11.011 Web Scraping of Fiest Tech Website Resource page
    09:00
  • 12.12 - 11.012 Assignment 02 demo
    02:11
  • 12.13 - 11.13 Quiz
    03:32
  • 12.14 - 11.014 Key takeaways
    05:25
  • 13.01 - 12.001 Why Big Data Solutions are Provided for Python
    07:28
  • 13.02 - 12.2 Hadoop Core Components
    02:11
  • 13.03 - 12.003 Python Integration with HDFS using Hadoop Streaming
    04:34
  • 13.04 - 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
    06:07
  • 13.05 - 12.5 Knowledge Check
    01:12
  • 13.06 - 12.006 Python Integration with Spark using PySpark
    03:32
  • 13.07 - 12.007 Demo 02 - Using PySpark to Determine Word Count
    09:00
  • 13.08 - 12.8 Knowledge Check
    02:32
  • 13.09 - 12.009 Determine the wordcount
    01:21
  • 13.10 - 12.010 Assignment 01 Demo
    02:32
  • 13.11 - 12.011 Display all the airports based in New York using PySpark
    03:32
  • 13.12 - 12.012 Assignment 02 Demo
    05:44
  • 13.13 - 12.13 Quiz
    01:12
  • 13.14 - 12.014 Key takeaways
    02:35
  • 14.01 - IBM HR Analytics Employee Attrition Modeling.
    01:08:09
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Data Science With Python Exam & Certification

Once you successfully complete the Data Science with Python training, Fiest Tech will provide you with an industry-recognized course completion certificate which will have a lifelong validity.

Online Classroom:

  • Attend one complete batch of Data Science with Python training.
  • Submit at least one completed project.

Online Self-Learning:

  • Complete 85% of the course
  • Submit at least one completed project.

 

Yes, we provide 1 practice test as part of our Data Science with Python course to help you prepare for the actual certification exam. You can try this Free Data Science with Python Practice Test to understand the type of tests that are part of the course curriculum.  

Bootcamp

Why Online Bootcamp

Introduction to Data Science

An overview of data science, its applications, and the data science workflow. This section introduces the fundamental concepts, methodologies, and tools used in data science projects.

Exploratory Data Analysis (EDA)

Methods for exploring and visualizing data to gain insights and identify patterns, trends, and relationships. EDA techniques include summary statistics, data visualization, and statistical graphics.

Data Visualization

Principles and best practices for creating effective and visually appealing visualizations to present data insights.

Model Evaluation and Validation

Techniques for evaluating and validating machine learning models, including cross-validation, performance metrics, overfitting, and model selection.

FAQS

Data Science With Python Course FAQs

Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.

Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of Data Science libraries and it is the ideal language for implementing algorithms and the rapid development of applications.

The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Fiest Tech's Python Data Science course provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.

  • Harvard Business Review has already named Data Scientist as the ‘Sexiest Job of the 21st Century.’ The statement is echoed in LinkedIn Emerging Jobs Report 2021 in which Data Science specialists are one of the top emerging jobs in the US with Python as one of its key skills. The job role has witnessed an annual growth of 35 percent for Data scientists and Data engineers.

If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python. However, it is not compulsory.

 

Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course.

 It is beneficial if you brush up your skills in core math, statistics, and programming basics to get started with this Data Science with Python course.

 

Yes, Python supports a lot of open-source libraries like SciPy, NumPy, Scikit-Learn, TensorFlow, Matplotlib, and Pandas

 Python is used for a variety of applications and you don’t need to be familiar with all of its libraries and modules. Even if you know the basics of Python, this Data Science with Python certification covers the popular libraries of Python that are used in data science projects. 

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