JumpStart to Python for Data Science

Data science is a fast growing new knowledge domain used by organizations to make data driven decisions. Data Scientists wear various hats to work with data and to derive value from it. The Python programming language is an indispensable tool for the data science practitioner and a must-know tool for every aspiring data scientist. Python offers you a fast, reliable, cross-platform, and mature environment for data analysis, machine learning, and algorithmic problem solving.

Beginning with the essentials of Python in data science, you ll learn to manage data and perform linear algebra in Python. You ll apply logistic regression techniques to your applications before creating recommendation engines with various collaborative filtering algorithms and improving your predictions by applying the ensemble methods. Finally, you ll perform K-means clustering, along with an analysis of unstructured data with different text mining techniques, and leveraging the power of Python in big data analytics.

This fast paced and technical course helps you move beyond the hype and transcend the theory by providing you with a hands-on study of data science.

Getting Started with Raw Data

• The world of arrays with NumPy
• Empowering data analysis with pandas
• Data cleansing
• Data operations

Inferential Statistics

• Various forms of distribution
• A z-score
• A p-value
• One-tailed and two-tailed tests
• Type 1 and Type 2 errors
• A confidence interval
• Correlation
• Z-test vs T-test
• The F distribution
• The chi-square distribution
• The chi-square test of independence
• ANOVA

Finding a Needle in a Haystack

• What is data mining?
• Presenting an analysis

Making Sense of Data through Advanced Visualization

• Controlling the line properties of a chart
• Creating multiple plots
• Playing with text
• Styling your plots
• Box plots
• Heatmaps
• Scatter plots with histograms
• A scatter plot matrix
• Area plots
• Bubble charts
• Hexagon bin plots
• Trellis plots
• A 3D plot of a surface

Uncovering Machine Learning

• Different types of machine learning
• Decision trees
• Linear regression
• Logistic regression
• The naive Bayes classifier
• The k-means clustering
• Hierarchical clustering

Performing Predictions with a Linear Regression

• Simple linear regression
• Multiple regression
• Training and testing a model

Estimating the Likelihood of Events

• Logistic regression

Generating Recommendations with Collaborative Filtering

• Recommendation data
• User-based collaborative filtering
• Item-based collaborative filtering

Pushing Boundaries with Ensemble Models

• The census income dataset
• Decision trees
• Random forests
• Applying Segmentation with k-means Clustering
• The k-means algorithm and its working
• The k-means clustering with countries
• Clustering the countries

Analyzing Unstructured Data with Text Mining

• Preprocessing data
• Creating a wordcloud
• Word and sentence tokenization
• Parts of speech tagging
• Stemming and lemmatization
• The Stanford Named Entity Recognizer
• Performing sentiment analysis on world leaders using Twitter

Leveraging Python in the World of Big Data

• What is Hadoop?
• Python MapReduce
• File handling with Hadoopy
• Pig
• Python with Apache Spark

Before attending this course, you should have:

• Written Python scripts
• Be comfortable working with files, folders, and the command line

Data Scientists, Data Analysts, Software Engineers, Data Engineers, and Developers.