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Learn data science with Python by building five real-world projects! In Data Science Bookcamp you’ll test and build your knowledge of Python and learn to handle the kind of open-ended problems that professional data scientists work on daily. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career.The book lives up to its promise of using code examples instead of going with heavy mathematics.
About the Technology
In real-world practice, data scientists create innovative solutions to novel open ended problems. Easy to learn and use, the Python language has become the de facto language for data science amongst researchers, developers, and business users. But knowing a few basic algorithms is not enough to tackle a vague and thorny problem. It takes relentless practice at cracking difficult data tasks to achieve mastery in the field. That’s just what this book delivers.About the book
Data Science Bookcamp is a comprehensive set of challenging projects carefully designed to grow your data science skills from novice to master. Veteran data scientist Leonard Apeltsin sets five increasingly difficult exercises that test your abilities against the kind of problems you’d encounter in the real-world. As you solve each challenge, you’ll acquire and expand the data science and Python skills you’ll use as a professional data scientist. Ranging from text processing to machine learning, each project comes complete with a unique downloadable data set and a fully-explained step-by-step solution. Because these projects come from Dr. Apelstin’s vast experience, each solution highlights the most likely failure points along with practical advice for getting past unexpected pitfalls. When you wrap up these five awesome exercises, you’ll have a diverse relevant skill set that’s transferable to working in industry.
Table of Contents takes you straight to the bookdetailed table of contents
Case study 1: Finding the Winning Strategy in a Card Game
1 Computing Probabilities Using Python
1.1 Sample Space Analysis: An Equation-Free Approach for Measuring Uncertainty in Outcomes
1.1.1 Analyzing a Biased Coin
1.2 Computing Non-Trivial Probabilities
1.2.1 Problem 1: Analyzing a Family with 4 Children
1.2.2 Problem 2: Analyzing Multiple Dice Rolls
1.2.3 Problem 3: Computing Dice-Roll Probabilities using Weighted Sample Spaces
1.3 Computing Probabilities Over Interval Ranges
1.3.1 Evaluating Extremes Using Interval Analysis
1.4 Summary
2 Plotting Probabilities Using Matplotlib
2.1 Basic Matplotlib Plots
2.2 Plotting Coin-Flip Probabilities
2.2.1 Comparing Multiple Coin-Flip Probability Distributions
2.3 Summary
3 Running Random Simulations in NumPy
3.1 Simulating Random Coin-Flips and Dice-Rolls Using NumPy
3.1.1 Analyzing Biased Coin-Flips
3.2 Computing Confidence Intervals Using Histograms and NumPy Arrays
3.2.1 Binning Similar Points in Histogram Plots
3.2.2 Deriving Probabilities from Histograms
3.2.3 Shrinking the Range of a High Confidence Interval
3.2.4 Computing Histograms in NumPy
3.3 Leveraging Confidence Intervals to Analyze a Biased Deck of Cards
3.4 Using Permutations to Shuffle Cards
3.5 Summary
4 Case Study 1 Solution
4.1 Overview
4.2 Predicting Red Cards within a Shuffled Deck
4.2.1 Estimating the Probability of Strategy Success
4.3 Optimizing Strategies using the Sample Space for a 10-Card Deck
4.4 Key Takeaways
Case study 2: Assessing Online Ad-Clicks for Significance
5 Basic Probability and Statistical Analysis Using SciPy
5.1 Exploring the Relationships between Data and Probability Using SciPy
5.2 Mean as a Measure of Centrality
5.2.1 Finding the Mean of a Probability Distribution
5.3 Variance as a Measure of Dispersion
5.3.1 Finding the Variance of a Probability Distribution
5.4 Summary
6 Making Predictions Using the Central Limit Theorem and SciPy
6.1 Manipulating the Normal Distribution Using SciPy
6.1.1 Comparing Two Sampled Normal Curves
6.2 Determining Mean and Variance of a Population through Random Sampling
6.3 Making Predictions Using Mean and Variance
6.3.1 Computing the Area Beneath a Normal Curve
6.3.2 Interpreting the Computed Probability
6.4 Summary
7 Statistical Hypothesis Testing
7.1 Assessing the Divergence Between Sample Mean and Population Mean
7.2 Data Dredging: Coming to False Conclusions through Oversampling
7.3 Bootstrapping with Replacement: Testing a Hypothesis When the Population Variance is Unknown
7.4 Permutation Testing: Comparing Means of Samples when the Population Parameters are Unknown
7.5 Summary
8 Analyzing Tables Using Pandas
8.1 Storing Tables Using Basic Python
8.2 Exploring Tables Using Pandas
8.3 Retrieving Table Columns
8.4 Retrieving Table Rows
8.5 Modifying Table Rows and Columns
8.6 Saving and Loading Table Data
8.7 Visualizing Tables Using Seaborn
8.8 Summary
9 Case Study 2 Solution
9.1 Overview
9.2 Processing the Ad-Click Table in Pandas
9.3 Computing P-values from Differences in Means
9.4 Determining Statistical Significance
9.5 41 Shades of Blue: A Real-Life Cautionary Tale
9.6 Key Takeaways
Case Study 3: Tracking Disease Outbreaks Using News Headlines
10 Clustering Data into Groups
10.1 Using Centrality to Discover Clusters
10.2 K-Means: A Clustering Algorithm for Grouping Data into K Central Groups
10.2.1 K-means Clustering Using Scikit-learn
10.2.2 Selecting the Optimal K Using the Elbow Method
10.3 Using Density to Discover Clusters
10.4 DBSCAN: A Clustering Algorithm for Grouping Data Based on Spatial Density
10.4.1 Comparing DBSCAN and K-means
10.4.2 Clustering Based on Non-Euclidean Distance
10.4.3 Limitations of the DBSCAN Algorithm
10.5 Analyzing Clusters Using Pandas
10.6 Summary
11 Geographic Location Visualization and Analysis
11.1 The Great-Circle Distance: A Metric for Computing Distances Between 2 Global Points
11.2 Plotting Maps Using Basemap
11.3 Location Tracking Using GeoNamesCache
11.3.1 Accessing Country Information
11.3.2 Accessing City Information
11.3.3 Limitations of the GeoNamesCache Library
11.4 Matching Location Names in Text
11.5 Summary
12 Case Study 3 Solution
12.1 Overview
12.2 Extracting Locations from Headline Data
12.3 Visualizing and Clustering the Extracted Location Data
12.4 Extracting Insights from Location Clusters
12.5 Key Takeaways
Case Study 4: Using Online Job Postings to Improve Your Data Science Resume
13 Measuring Text Similarities
13.1 Simple Text Comparison
13.1.1 Introduction to the Jaccard Similarity
13.1.2 Replacing Words with Numeric Values
13.2 Vectorizing Texts Using Word Counts
13.2.1 Using Normalization to Improve TF Vector Similarity
13.3 Matrix Multiplication for Efficient Similarity Calculation
13.3.1 Basic Matrix Operations
13.3.2 Computing All-By-All Matrix Similarities
13.4 Computational Limits of Matrix Multiplication
13.5 Summary
14 Dimension Reduction of Matrix Data
14.1 Clustering 2D Data in 1-Dimension
14.1.1 Reducing Dimensions Using Rotation
14.2 Dimension Reduction Using PCA and Scikit-Learn
14.3 Clustering 4D Data in 2-Dimensions
14.3.1 Limitations of PCA
14.4 Computing Principal Componets Without Rotation
14.4.1 Extracting Eigenvectors Using Power Iteration
14.5 Efficient Dimension Reduction Using SVD and Scikit-Learn
14.6 Summary
15 NLP Analysis of Large Text Datasets
15.1 Loading Online Forum Discussions Using Scikit-Learn
15.2 Vectorizing Documents Using Scikit-Learn
15.3 Ranking Words by Both Post-Frequency and Count
15.3.1 Computing TFIDF Vectors with Scikit-Learn
15.4 Computing Similarities Across Large Document Datasets
15.5 Clustering Texts by Topic
15.5.1 Exploring a Single Text Cluster
15.6 Visualizing Text Clusters
15.6.1 Using Subplots to Display Multiple Word Clouds
15.7 Summary
16 Extracting Text from Web Pages
16.1 The Structure of HTML Documents
16.2 Parsing HTML using Beautiful Soup
16.3 Downloading and Parsing Online Data
16.4 Summary
17 Case Study 4 Solution
17.1 Overview
17.2 Extracting Skill Requirements from Job Posting Data
17.2.1 Exploring the HTML for Skill Descriptions
17.3 Filtering Jobs by Relevance
17.4 Clustering Skills in Relevant Job Postings
17.4.1 Grouping the Job Skills into 15 Clusters
17.4.2 Investigating the Technical Skill Clusters
17.4.3 Investing the Soft-Skill Clusters
17.4.4 Exploring Clusters at Alternative Values of K
17.4.5 Analyzing the 700 Most-Relevant Postings
17.5 Conclusion
17.6 Key Takeaways
Case Study 5: Social Circle Detection in Facebook Data
18 Graph Theory and Network Analysis
19 Network-driven Supervised Machine Learning
20 Training Linear Classifiers with Logistic Regression.
21 Training Non-Linear Classifiers with Decision Tree Techniques
22 Case Study 5 Solution
What's inside
- Five in-depth Python exercises with full downloadable data sets
- Web scraping for text and images
- Organise data sets with clustering algorithms
- Visualize complex multi-variable datasets
- Train a decision tree machine learning algorithm
About the reader
For readers who know the basics of Python. No prior data science or machine learning skills required.About the author
Leonard Apeltsin is a senior data scientist and engineering lead at Primer AI, a startup that specializes in using advanced Natural Language Processing techniques to extract insight from terabytes of unstructured text data. His PhD research focused on bioinformatics that required analyzing millions of sequenced DNA patterns to uncover genetic links in deadly diseases.placing your order...
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