Data Science with Python Training Certification Course Online

SKU: 8401
12 Lesson
|
40 Hours
5 (1 reviews)
igmGuru’s Data Science with Python Course online is designed to help you gain the skills needed to work with Python to fulfill Data Science tasks. In our Data Science with Python online course program, we cover various essential Python libraries like Pandas, Seaborn, NumPy, and Matplotlib. You will learn data analytics, data visualization concepts, and regression models. Get a Data Science with Python certification course as you showcase knowledge in data transformation, histograms, and different types of distributions.

Data Science with Python Course Overview

igmGuru's Data Science with Python Course Online has been crafted by some of the best subject matters experts in the industry and the faculty who are teaching at some of the best universities. The Data Science with Python Course online is crafted for professionals with different level of experiences, from beginners to advanced programmers, and can be delivered in different formats such as online classroom or self-paced. This python language course is embedded with topics and techniques which are practiced and are in demand in the industry. Thus, this Python course online is a good fit for anyone who wishes to get ahead in the field of Data Science with Python.

The demand and the supply gap for data scientists is huge and with igmGuru's Data Science with python course for beginners, you can be the one who benefits from this gap. As per the Bureau of Labor Statistics , the growth percentage for employment of data scientists is projected to grow around 35% between the years 2022 and 2032. Python, as a programming language, is immensely popular for building Data Science-based applications owing to its simplicity, large community support, and ease of deployment in the development projects.

Data Science with Python online course by igmGuru is crafted by keeping in mind about learners who have nil to some level of exposure to Python. Ideally, you should dedicate a good amount of time to understand the theoretical part. Application of theoretical concepts by doing hands-on statistical techniques should become the next focus. This python language course provides the learner with multiple data sets for practicing during the session, which can also be used to practice later on in the form of self-study. This Python course for beginners will help you in your journey of mastering Data Science with Python.

The three main pillars of Data Science with python Online training are

  1. Application of mathematical and statistical concepts
  2. Expressing them using a programming language or a tool/platform
  3. Particular business domain

The python language course focus on multiple use cases, some popular applications/services using Python, and understanding Data Science workflow using Python theoretically. We aid you in understanding the basic components of a Data Science model, starting with fetching data from your DB and moving to building a model that is in a deployable form.

Key deliverables of Data Science with Python Course Online?

As you will Learn Python Course Online, you will learn the below mentioned things

    1. Statistics for data science
    2. Basic data cleaning techniques for model building
    3. Converting the raw data into a format that is machine consumable
    4. Working principle of ML models and their applicability
    5. Understanding the parameters required for checking model accuracy
    6. Making the model available as a service by deploying it
    7. Maintaining the model in a long run

With respect to the above points, you will also learn

  • How to use Data Science specific libraries in Python. For eg. frequently used libraries in data cleaning like NumPy, pandas, spicy, groupby, merge
  • Using data plotting libraries like matplotlib, seaborn; machine learning-based modules available inside scikit learn for building various regression and classification based algorithms
  • Using libraries to check model accuracy like confusion matrix, MSE, RMSE, Natural Language-based libraries like NLTK, genism, VADER.

A good amount of content has also been dedicated to Natural Language Processing (NLP) techniques and various web scraping methodologies. NLP has been gaining a lot of popularity lately, owing to its use in our day-to-day life. Mails, tweets, FB posts, and WhatsApp chats are ideal input for any NLP-based model. You will become experienced in NLP-based openings, which are nowadays considered to be a specialty within the Machine Learning branch. Hence, assessing the market-based demands, we have specifically designed modules to upskill you in this area as well. A very significant model in the area of NLP is Sentiment Analysis, which is something we will be building to start things off and will move on to build a much complex algorithm in this area.

igmGuru's Data Science with Python course online provides
  1. An overview of the Python programming language and its basic syntax, data types, and control structures
  2. It also covers techniques for working with data using the Pandas library, such as data cleaning, filtering, and manipulation
  3. Furthermore, the program explores methods for analyzing and visualizing data with Python libraries (e.g. NumPy, Matplotlib, Seaborn)
  4. Machine learning algorithms (e.g. supervised and unsupervised learning) implemented through libraries like scikit-learn
  5. Topics like natural language processing (NLP), deep learning or big data analysis which may also be included in more advanced classes
  6. Hands-on exercises and projects are available to give students a chance to apply what they have learned while tailoring to specific industries (for example finance or healthcare) or use cases such as predictive modeling or anomaly detection is also possible.

The Data Science with Python Course Online program includes hands-on exercises on projects. Students can apply the concepts they have learned in the training. This Python Certification course will help you move ahead in specific industries, such as finance or healthcare, and include use cases, such as predictive modeling, and or anomaly detection.

Key Features

Data Science With Python Training Modules

1. Data Science
2. Data Scientists
3. Examples of Data Science
4. Python for Data Science

1. Introduction to Data Visualization
2. Processes in Data Science
3. Data Wrangling, Data Exploration, and Model Selection
4. Exploratory Data Analysis or EDA
5. Data Visualization
6. Plotting
7. Hypothesis Building and Testing

1. Introduction to Statistics
2. Statistical and Non-Statistical Analysis
3. Some Common Terms Used in Statistics
4. Data Distribution: Central Tendency, Percentiles, Dispersion
5. Histogram, Bell Curve, Hypothesis Testing
6. Chi-Square Test, Correlation Matrix, Inferential Statistics

1. Introduction to Anaconda
2. Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
3. Jupyter Notebook Installation, Jupyter Notebook Introduction
4. Variable Assignment
5. Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
6. Creating, accessing, and slicing tuples
7. Creating, accessing, and slicing lists
8. Creating, viewing, accessing, and modifying dicts
9. Creating and using operations on sets
10. Basic Operators: 'in', '+', '*', Functions, Control Flow

1. NumPy Overview
2. Properties, Purpose, and Types of ndarray
3. Class and Attributes of ndarray Object
4. Basic Operations: Concept and Examples
5. Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
6. Copy and Views, Universal Functions (ufunc)
7. Shape Manipulation, Broadcasting, Linear Algebra

1. SciPy and its Characteristics, SciPy sub-packages
2. SciPy sub-packages –Integration, SciPy sub-packages – Optimize
3. Linear Algebra
4. SciPy sub-packages – Statistics, SciPy sub-packages – Weave
5. SciPy sub-packages - I O

1. Introduction to Pandas
2. Data Structures, Series, DataFrame, Missing Values
3. Data Operations, Data Standardization
4. Pandas File Read and Write Support
5. SQL Operation

1. Introduction to Machine Learning
2. Machine Learning Approach
3. How Supervised and Unsupervised Learning Models Work
4. Scikit-Learn
5. Supervised Learning Models - Linear Regression, Logistic Regression
6. K Nearest Neighbors (K-NN) Model
7. Unsupervised Learning Models: Clustering, Dimensionality Reduction
8. Pipeline, Model Persistence, Model Evaluation - Metric Functions

1. NLP Overview
2. NLP Approach for Text Data
3. NLP Environment Setup
4. NLP Sentence analysis, NLP Applications
5. Major NLP Libraries, Scikit-Learn Approach
6. Scikit - Learn Approach Built - in Modules, Scikit - Learn Approach Feature Extraction
7. Bag of Words, Extraction Considerations
8. Scikit - Learn Approach Model Training
9. Scikit - Learn Grid Search and Multiple Parameters
10. Pipeline

1. Introduction to Data Visualization
2. Python Libraries, Plots
3. Matplotlib Features: Line Properties Plot with (x, y), Controlling Line Patterns and Colors, Set Axis, Labels, and Legend Properties, Alpha and Annotation, Multiple Plots, Subplots
4. Types of Plots and Seaborn

1. Web Scraping
2. Common Data/Page Formats on The Web
3. The Parser, Importance of Objects
4. Understanding the Tree, Searching the Tree
5. Navigating options, Modifying the Tree
6. Parsing Only Part of the Document
7. Printing and Formatting, Encoding

1. Need for Integrating Python with Hadoop
2. Big Data Hadoop Architecture
3. MapReduce, Cloudera QuickStart VM Set Up
4. Apache Spark
5. Resilient Distributed Systems (RDD)
6. PySpark, Spark Tools
7. PySpark Integration with Jupyter Notebook

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Data Science with Python Course Fees

SELF PACED LEARNING

US $ 199.00
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  • Duration : 40 hrs
  • Lifetime Free Upgrade
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Online Class Room Program

US $ 699.00
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  • Duration : 40 Hrs
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Classes Starting From
  • Fast Track Batch 21 Jul 2024
  • Weekday Batch 22 Jul 2024
  • Weekend Batch 27 Jul 2024

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Data Science With Python Certification Exam

How to get data science with python certification in 2024?

igmGuru's Data Science with Python certification course has been designed to polish your knowledge and skills in Python for data science tasks, such as data cleaning, data manipulation, data analysis, and machine learning. The format, content and prerequisites of the Python certification exam may differ depending on the certifying organization or institution.

Python certification exams include multiple-choice questions, and coding challenges or projects, or a combination of both. The commonly covered topics are:

  1. Programming Python language, and its basic syntax, and data types, control structures
  2. Python Data manipulation using libraries like NumPy, and Pandas
  3. Data visualization and analysis, using libraries like Matplotlib, Seaborn
  4. Machine learning knowledge and algorithm, implementation, using libraries like scikit-learn

Additional topics like deep learning, natural language processing, and big data analysis are also covered. The Python Certification exam has a time limit and it could be proctored or online. Some certifying bodies may have certain prerequisites for the exam, such as prior knowledge of Python or Data Science fundamentals, some experience in the field, or a base certification.

Passing the Data Science with Python certification exam and earning the certification demonstrates that you have the knowledge and skills required to work with Python for data science tasks and projects.

Popular Python for Data Science certifications include PCEP, PCAP, and PCAT from the Python Institute.

Data Science With Python Certification Exam

Data Science with Python Online Course FAQ