MLTrain, "Python for Big Data Analytics and Machine Learning 101"

Ismion MLTrain

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<H2><A HREF="https://mltrain.cc/events/python-for-big-data-analytics-and-machine-learning-101/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">MLTrain in Athens</A></H2>
<H3>Duration: 16Hrs (Thursday 14:00-18:00, Friday 14:00-18:00, Saturday 09:00-17:00)</H3>
<P>Learn the fundamentals of data science with Python, whether working on your laptop or a big data cluster, using numpy, pandas and pySpark. Communicate your analyses using informative graphics from matplotlib and seaborn. Configure, train and assess machine learning models with scikit-learn.</P>
<H2>Overview</H2>
<P>Development on contemporary machine learning and cluster computing frameworks is geared towards Python. Even when Python is not a framework’s primary API, there is always a python binding. A notable example is Spark whose primary API is Scala but is most often used through its Python binding.</P>
<P>Python is also the language most data scientists prefer for desktop data analysis. There’s a rich set of ready-made ML algorithms and libraries to pull data from and push to large storage backends in different formats, making the whole process of Exploratory Data Analysis (EDA) effective and easy.</P>
<P>This course is a 3-day hands-on lab on Python's numpy, pandas, pySpark, matplotlib, seaborn and scikit-learn packages, a de facto data scientist’s toolset standard. Along the way we’ll test our knowledge with exercises using real-life datasets from Kaggle<BR>and elsewhere.</P>
<H2>Objectives</H2>
<P>Upon course completion, the participants will know</P>
<UL>
<LI>the essential statements, constructs and idioms of Python and how to develop and share their code using Jupyter<BR>notebooks.</LI>
<LI>the basics of numpy and pandas libraries for querying in-memory tabular data.</LI>
<LI>how to visualize the outcomes of data analyses using matplotlib and seaborn.</LI>
<LI>They will also learn how to process data on large clusters using PySpark and setup and assess machine learning models with scikit-learn.</LI>
</UL>
<H2>Who should attend</H2>
<OL>
<LI>Software engineers who want to make a transition to data science practice.</LI>
<LI>Data scientists who want to learn about the Python data-analysis and machine learning toolset.</LI>
<LI>Business Analysts who want to take an evolutionary leap to big data analytics.</LI>
<LI>Technical managers involved in the evaluation of technologies and human resources related to analytics and big data.</LI>
</OL>
<H2>Prerequisites</H2>
<OL>
<LI>Knowledge of installing and configuring computer software.</LI>
<LI>Understanding of computer programming concepts.</LI>
<LI>General knowledge of data formats and data transformations (filtering and reduction)</LI>
<LI>Basic knowledge of python and basic descriptive statistics is helpful but not mandatory.</LI>
<LI>A laptop with Ubuntu 16.04 or windows 10 OS, at least 8GB RAM and 12GB of free disk storage.</LI>
</OL>
<H2>Course Outline</H2>
<OL>
<LI><STRONG>Development front-ends: jupiter console, jupyter notebook and qtconsole</STRONG><BR>Using the command history<BR>Interacting with the OS<BR>The interactive debugger</LI>
<LI><STRONG>Python bootcamp</STRONG><BR>Literals, expressions and statements<BR>Python containers, comprehensions and generator expressions<BR>Function objects, lambdas and closures</LI>
<LI><STRONG>Fast array calculations with the numpy package</STRONG><BR>The ndarray object<BR>Universal functions<BR>Integer and boolean Slicing<BR>Set logic</LI>
<LI><STRONG>Tabular data management with the pandas package</STRONG><BR>Indexing, selection and filtering<BR>Function application and mapping<BR>Data filtering and reductions<BR>Handling missing data<BR>Hierarchical indexing</LI>
<LI><STRONG>Cluster computing with Spark and PySpark</STRONG><BR>Installing and configuring Spark over Spark’s standalone cluster<BR>Dataframes and untyped operations<BR>running SQL programmaticaly<BR>schema objects and types<BR>aggregations</LI>
<LI><STRONG>Plotting with matplotlib and Seaborn packages</STRONG><BR>Matplotlib API primer<BR>Figures, subplots, axes, lines and markers<BR>Line and bar plots<BR>Histograms and density plots<BR>Scatter plots</LI>
<LI><STRONG>Introduction to scikit-learn<BR></STRONG>Decision trees<BR>Random forests<BR>Gradient boosted trees</LI>
</OL></DIV>
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Hellenic American Education Center

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MLTrain, "Python for Big Data Analytics and Machine Learning 101"

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