JumpStart to Python for Data Science Course Details:

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.

    No classes are currenty scheduled for this course.

    Call (919) 283-1653 to get a class scheduled online or in your area!

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

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

*Please Note: Course Outline is subject to change without notice. Exact course outline will be provided at time of registration.

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.

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