Big Data Analytics

Big Data Analytics Courses

Data Cleaning and Manipulation 

How do we clean the raw data, deal with missing data, and perform transformations on certain variables? Much of the raw data contained in large data sets is un-preprocessed, incomplete, and noisy. For example, a data set may contain fields that are obsolete or redundant, missing values, outliers, and data in a form not suitable for the data mining models. Analyses carried out on un-preprocessed data can lead to erroneous conclusions.

Thus, in order to be useful for data mining purposes, the databases need to undergo preprocessing and depending on the data set, this alone can account for 10–60% of all the time and effort for the entire data mining process. 

Therefore, in this session, we will introduce a very powerful Python tool, pandas, which has a great number of features and capabilities to manipulate large data sets. No programming knowledge is assumed, rather we will start with a short introduction to Python.

Who Should Attend:

Managers, data analysts, and others who need to keep abreast of the latest methods for enhancing return on investment.

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Exploratory Data Analysis and Data Visualization 

Especially when confronted with big data sets we often do not have a clear understanding how different attributes are related or even distributed, so we use exploratory data analysis (EDA), or graphical data analysis. 

EDA allows us to delve into the data, to discover relationships among the attributes, and to identify interesting subsets of the observations.

In this advanced training using the Python programming language we will be analyzing, summarizing, and reporting data, allowing you to extend your skills to industry strength analytics and to develop capabilities to make better business decisions to help reduce costs, increase profits, or better manage operations.

Who Should Attend:

Managers, data analysts, and others who need to keep abreast of the latest methods for enhancing return on investment.

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Supervised and Unsupervised Machine Learning 

Corporations and institutions worldwide are learning to apply data mining and predictive analytics, in order to increase profits. A significant constraint on realizing value from big data is a shortage of talent, particularly of people with deep expertise in statistics and machine learning, and the managers and analysts who know how to operate companies by using insights from big data.

We will discuss step-by-step hands-on solutions using Python with scikit-learn of real-world business problems using widely available data mining techniques in the area of supervised and unsupervised learning applied to real-world data sets.

Who Should Attend:

Managers, data analysts, and others who need to keep abreast of the latest methods for enhancing return on investment.

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