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Python for Data Science course provides introduction to Python for Data Science and programming in general. Python for Data Science course covers all the aspects of practical data science to make you a competitive data analyst. In era of big data; businesses across the world use Python to leverage the available information benefit. By storing; managing; manipulating and filtering the data in Python, you can give a competitive edge to your organization as well a boost to your career.
During the course, you will learn Python as the most used programming language for the particular domain of Data Science. During Python for Data Science training, you will master the techniques of deploying Python for Data Science; working with Pandas library for Data Science; data munging and data cleaning etc in addition to learning advanced numeric analysis with real-world hands-on project handling experience through case studies.
For whom ‘Python for Data Science’ is the Must to Join Course:
It is the best pick as the career booster course for BI Managers; Project Managers; Software Developers; ETL Professionals; Analytics Professionals and Big Data Professionals. It is must to have qualification for the data analysts looking to improve their expertise with technical excellence of Python.
Learning Objectives of ‘Python for Data Science’ Course:
Future Prospects of ‘Python for Data Science’ Certification:
We at Microtek Learning provide three days of comprehensive training for Python for Data Science course following the most detailed syllabus and robust lab activities:
1. Introduction to Data Science
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
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.
Mathematical computing in Python; arrays & matrices; array indexing; array math; ND-array object; datatypes; standard deviation; conditional probability in NumPy; correlation; covariance
Introduction to SciPy; building on top of NumPy; characteristics of SciPy; sub- packages for SciPy; Bayes Theorem with SciPy
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.
Using Matplotlib for plotting graphs and charts like Scatter; Bar; Pie; Line; Histogram and more; Matplotlib API; Subplots and Pandas built-in data visualization.
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.
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.
Analyzing the naming pattern using Python; Python Web Scraping for Data Science; Predicting customer churn in Telecom Company; Server logs/Firewall logs
Python for Data Science course training involves intensive lab tests and projects assignments.
Although the Python for Data Science course starts with introduction of Python in Data Science domain, still, the basic knowledge of Python is good to have. Introduction to Python Programming, 4820 Mastering Python Programming or 4850 Advanced Python Programming like courses will be of great help to understand the advanced topics of Python for Data Science course as well as to add values to new credential.
Q. Can I switch from self-paced training to instructor-led virtual training?
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.
Q. Will I get any certificate also after completing the 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.
Q. Why should I learn Python from Microtek Learning?
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.
Q. What is the practical importance of data science projects?
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.