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, and Lambda functions 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, and 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.
Years of Experience
What you will learn
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 this course?
This training is intended for BI Managers, ETL Professionals, Software Developers, Big Data Professionals, and Analytics Professionals.
However, it's mandatory to have qualifications for data analysts who are seeing to advance their skills in technical aspects of Python.
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For dates, times, and location customization of this course, get in touch with us.
You can also speak with a learning consultant by calling 800-961-0337.
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.
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.
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.
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.
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.
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.
What is supervised learning; classification; Decision Tree; algorithm for Decision Tree induction; Confusion Matrix; Random Forest; NaÃ¯ve Bayes; working of NaÃ¯ve Bayes; how to implement NaÃ¯ve 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.
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
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
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.
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 yearsâ€™ 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.