JumpStart to Python for 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

Join an engaging hands-on learning environment, where you’ll:

• Manage data and perform linear algebra in Python
• Derive inferences from the analysis by performing inferential statistics
• Solve data science problems in Python
• Create high-end visualizations using Python
• Evaluate and apply the linear regression technique to estimate the relationships among variables
• Build recommendation engines with the various collaborative filtering algorithms
• Apply the ensemble methods to improve your predictions
• Work with big data technologies to handle data at scale

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

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.