Data Science with Python Training

• 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.

• 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

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.