The recent developments in cloud computing, graphics cards, cheaper memory and faster processors have made analyzing and interpreting big data possible.
In this 5 days course, we will use Python as a programming language for data analysis. We chose Python, because it is a dynamically typed high-level language. Python’s powerful modules make it one of the most popular and versatile languages of today. Not all data can be processed as is. So, we use Pandas, a Python library to prepare data and obtain insights into the data. We will then discuss data science techniques to classify or predict with examples using Python’s Scikit-learn module. The hands-on activities provide students an opportunity to practice and understand the concepts. We will consider examples from cyber security field.
Duration:
5 Days
Audience:
Data Scientists, Engineers
Prerequisites:
None
Course outline:
<p><strong>Early Registration : $1500 ( Ends Sept 15th, 2017)</strong></p>
<p><strong>Regual Registration : $1700 (After Sept 15th, 2017)</strong></p>
<p><strong>Dates : Monday Oct 23rd to Friday Oct 28th from 9:30 AM - 4:30 PM with lunch break from 12:30 PM - 1:30 PM. </strong></p>
<p><strong>Location : 940 Stewart Dr #205, Sunnyvale, CA </strong></p>
<p><strong>Lunch Included with registration.</strong></p>
<p>The recent developments in cloud computing, graphics cards, cheaper memory and faster processors have made analyzing and interpreting big data possible.</p>
<p>Our week-long course provides developers the necessary training to solve data science problems using Pandas and Sklearn. </p>
<p>In this course, we will use Python as a programming language for data analysis. We chose Python, because it is a dynamically typed high-level language. Python’s powerful modules make it one of the most popular and versatile languages of today. Not all data can be processed as is. So, we use Pandas, a Python library to prepare data and obtain insights into the data. We will then discuss data science techniques to classify or predict with examples using Python’s Scikit-learn module. The hands-on activities provide students an opportunity to practice and understand the concepts. We will consider examples from cyber security field.</p>
<p><strong>We are offering a hands-on course:</strong></p>
<ul>
<li>On-site</li>
<li>Project based approach</li>
<li>Video recording of the class will be available</li>
<li>Your own personalized, dedicated, 24X7 Lab available for hands on exercises and practice.</li>
</ul>
<p><strong>Learning Objectives:</strong> At the conclusion of the course students should be able to:</p>
<ul>
<li>Use Python distribution and any IDE to write Python code</li>
<li>Use Jupyter notebooks</li>
<li>Efficiently code using Python constructs</li>
<li>Define functions and use them</li>
<li>Create Pandas structures</li>
<li>Read data from csv, excel or url</li>
<li>Transform and prepare data</li>
<li>Visualize and perform statistical analysis on data</li>
<li>Use Linear regression techniques to predict data</li>
<li>Use classification techniques to classify data</li>
</ul>
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<p>Hands on Labs: Students will work through the following lab exercises:</p>
<p><strong>Python</strong></p>
<ul>
<li>Create data structures list, tuples and dictionaries.</li>
<li>Manipulate data structures</li>
<li>Convert one data structure to another </li>
<li>Define functions</li>
<li>Use Regex to parse log files.</li>
</ul>
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<p><strong>Pandas</strong></p>
<ul>
<li>Create data series from data frames.</li>
<li>Read csv file and choose specific columns for calculation.</li>
<li>Manipulate data frames and series.</li>
<li>Visualize data.</li>
<li>Obtain Statistics to understand the data.</li>
</ul>
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<p><strong>Data Science</strong></p>
<ul>
<li>Fit a Linear Regression equation on data and make predictions.</li>
<li>Determine probability using Logistic Regression and classify data.</li>
<li>Use K-means clustering to group data.</li>
<li>Perform Decision tree to classify data.</li>
<li>Perform Random forest ensemble to classify data.</li>
</ul>
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<p> </p>
<p><strong>Course Outline</strong> - This course is divided into three parts:</p>
<p><!--[if !supportLists]-->Python </p>
<ul>
<li>Setting up Python distribution, Pandas and Scikit-learn</li>
<li>Basic Python syntax</li>
<li>Reading and writing to text, csv and Excel files</li>
<li>Data structures – list, tuples and dictionaries</li>
<li>For, while and if</li>
<li>Functions</li>
<li>Regular Expression</li>
</ul>
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<p>Data Preparation and Analysis using Pandas </p>
<ul>
<li>What is Pandas?</li>
<li>Data Frames</li>
<li>Data Series</li>
<li>Reading data from csv files</li>
<li>Manipulating rows and columns – sort, filter, add missing values, create dummy variables</li>
<li>Visualization</li>
<li>Statistics</li>
<li>Getting data from a website</li>
<li>Analyze Apache access logs using regex and pandas</li>
</ul>
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<p>Data Science using Scikit-Learn </p>
<ul>
<li>What is Data Science?</li>
<li>Prediction vs Classification</li>
<li>Predict – Linear Regression</li>
<li>Logistic Regression classifier</li>
<li>K-Means Clustering</li>
<li>Decision Tree</li>
<li>Random Forest</li>
</ul>
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<p> </p>
<p><strong>Instructor Bio</strong> – Sridevi Pudipeddi is an expert in image processing, data science and Python. She currently teaches Advance Python at University of California Santa Cruz, Silicon Valley Extension, Santa Clara, CA. She also conducts corporate training in Python and its libraries. She has a Ph.D. in Mathematics and worked as a faculty. Organizer of All Things Python meetup group in Sunnyvale and co-author of "Image Processing and Acquisition using Python," - Chapman & Hall/CRC Press.</p>
