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Data Science with Python Training

What Data Science with Python training is all about?

Data Science with Python Training is developed to help professionals master Data Science Analytics by using Python. This technical course teaches you to operate on different python libraries like NumPy, SciPy, Lambda function to name a few. It contains all the essential aspects of practical data science that help you become a competitive data analyst. With this course, you will master leading data science analyst skills in domains like Data Science, Big Data, and Machine Learning.

This program teaches you about OOP concepts, functions, and expressions and how to create Pig and Hive UDF in Python. It provides in-depth information regarding SQLite, operations classes and guides you to deploy Python for MapReduce programming. Python is a leading programming language, and by the end of this course, your grip and skills on this language will improve, which will boost your credibility and help you prepare for the Data Science Certification exam.


Contact us to customize this class with your preferred dates, times and location.
You can call us on 1-800-961-0337 or Chat with our representative.

What are the course objectives for Data Science with Python training?
  • Learn about real-world Python for Data Science Projects,
  • How to deploy Pythons for Map Reduce Programming.
  • How to create Hive UDF in Python.
  • How to create Pig.
  • How to solve OOP functions, concepts and expressions.
  • Learn about SQLite Python; operations and classes.
  • What is Python for Data Scientists?
Who should attend Data Science with Python training?

This training is intended for BI Managers, ETL Professionals, Software Developers, Big Data Professionals and Analytics Professionals. However, it’s mandatory to have qualification for data analysts who are seeing to advance their skills in technical aspects of Python.

What is the course outline for Data Science with Python training?
  • 1. Introduction to Data Science
  • a). What is Data Science; what does a data scientist do; how Python is deployed for Data Science; Data Science process like data wrangling; data exploration and selecting the model; data visualization; exploratory data analysis; building hypothesis; plotting and other techniques.
  • 2. Introduction to Python
  • a). Important Python features; how is Python different from other programming languages; Python installation; Anaconda Python distribution for Windows; Linux and Mac; how to run Python script; Python IDE working mechanism; Python basic commands; Python variables; data types and keywords.
  • 3. Python Basic Constructs:
  • a). Basic construct in Python; uindentation like tabs and spaces; code comments; names and variables; Python built-in data types like containers; numeric; text sequence; constants and others; basic operators in Python; loop and control statements and more.
  • 4. Writing OOP in Python and Connecting To Database
  • a). OOP paradigm; inheritance; polymorphism and abstraction; access modifiers; instances; class members; classes and objects; function parameter and return type functions; Lambda expressions; connecting database to pull the data.
  • 5. NumPy for Mathematical Computing
  • a). Mathematical computing in Python; arrays & matrices; array indexing; array math; ND-array object; datatypes; standard deviation; conditional probability in NumPy; correlation; covariance
  • 6. Scipy for Scientific Computing
  • a). Introduction to SciPy; building on top of NumPy; characteristics of SciPy; sub- packages for SciPy; Bayes Theorem with SciPy
  • 7. Data Analysis and Machine Learning (Pandas)
  • a). Introduction to Machine Learning with Python; various tools in Python used for Machine Learning process flow of Machine Learning; categories of Machine Learning; understanding Linear Regression and Logistic Regression; gradient descent in Machine Learning; introduction to Python DataFrames; importing data from JSON, CSV, Excel, SQL database; NumPy array to DataFrame; various data operations; missing values; time series analysis.
  • 8. Data Manipulation
  • a). Data object; basic functionalities; using Pandas library for data manipulation; NumPy dependency of Pandas library; loading and handling data with Pandas; merging data objects; concatenation and various types of joins on data objects; exploring & analyzing datasets.
  • 9. Data visualization with Matplotlib
  • a). Using Matplotlib for plotting graphs and charts like Scatter; Bar; Pie; Line; Histogram and more; Matplotlib API; Subplots and Pandas built-in data visualization.
  • 10. Supervised Learning
  • a). What is supervised learning; classification; Decision Tree; algorithm for Decision Tree induction; Confusion Matrix; Random Forest; Naïve Bayes; working of Naïve Bayes; how to implement Naïve Bayes Classifier; Support Vector Machine; working process of Support Vector Mechanism; Hyperparameter Optimization; comparing Random Search with Grid Search; implementing Support Vector Machine for classification.
  • 11. Unsupervised Learning
  • a). Introduction to unsupervised learning; K-means clustering; K-means clustering algorithm; optimal clustering; hierarchical clustering and K-means clustering; hierarchical clustering; natural language processing; working with NLP on text data; using Jupyter Notebook; analyzing sentence; Scikit-Learn Machine Learning algorithms; bags of words model; extracting feature from text; searching a grid; model training; multiple parameters and building of a pipeline
  • 12. Web Scraping with Python
  • a). Web scraping in Python; various web scraping libraries; BeautifulSoup; Scrapy Python packages; installing of BeautifulSoup; installing Python parser lxml; creating soup object with input HTML; searching of tree; full or partial parsing; output print and searching the tree
  • 13. 
  • 14. Python integration with Hadoop and Spark
  • a). Integrating Python with Hadoop and Spark; Hadoop ecosystem; Hadoop Common; MapReduce and HDFS; Python coding for MapReduce jobs on Hadoop framework; Apache Spark; setting up Cloudera QuickStart VM; Spark tools; RDD in Spark; PySpark; integrating PySpark with Jupyter Notebook; Artificial Intelligence and Deep Learning; Deploying Spark code with Python; Machine Learning library of Spark MLlib; Deploying Spark MLlib for classification; Clustering and regression.
  • 15. Python for Data Science Projects
  • a). Analyzing the naming pattern using Python; Python Web Scraping for Data Science; Predicting customer churn in Telecom Company; Server logs/Firewall logs

You can definitely switch from self-paced learning to online instructor-led training. Just check our next scheduled class for Python for Data Science course.

Once you complete Python for Data Science course training at Microtek Learning along with all the real-world projects, quizzes and assignments and score at least 60% marks in qualifying exam, we will issue a widely recognized certificate.

We provide you maximum opportunities to have hands-on experience during the training. When you complete Python for Data Science course at Microtek Learning, your credentials are acknowledged wider and better. In addition, we have the best trained instructors with years’ experience in Python arena.

By completing the data science projects at your own by applying the learned skills, you get the opportunity the test your practical skill and knowledge; as well as it boosts your confidence to take on a job just after completing the course.

5 Days | $ 2000
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1632 Learners

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