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Data Science With Python
The Data Science with Python certification course provides a complete overview
of Python's Data Analytics tools and techniques. Learning Python is a crucial
skill for many Data Science roles. Acquiring knowledge in Python will be the key
to unlocking your career as a Data Scientist. This Data Science with Python
certification course gives you a complete overview of Python’s data analytics
tools and techniques. Learning python is a crucial skill for many data science roles
and you can develop it with this Python data science course. With a blended learning
approach, you can learn Python for data science along with concepts like data wrangling
mathematical computing, and more. Unlock your career as a data scientist.
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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
At Fiesttech, we value the trust of our patrons immensely. But, if you feel that this Data Science With Python does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
- 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
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.
Annual Salary
Hiring Companies
Annual Salary
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Annual Salary
Hiring Companies
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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!
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.
Course Content
Live Course
Self Paced
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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.
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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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