Data Analytics Bootcamp
Analytics Domain has trained more than 1500 professionals from over 100+ companies around the world through a variety of workshops and boot camps.
Our trainings are online, hands-on, and feedback oriented we take personal interest in each students learning needs.
Our students are exposed to real world scenarios for solving real world problems right from week one.
Write to - email@example.com, to learn more.
This course covers foundational techniques and tools required for data science and big data analytics. The course focuses on concepts, principles, and techniques applicable to any technology environment and industry and establishes confidence in the student to tackle many types of situations related to Data Science.
Course OverviewThis course introduces the Participants to concepts of Data Science, Data mining techniques and the underlying statistics that support big data analytics. The course is vast and each topic of training is divided into modules so that participants can learn, understand and practice each module and develop mastery of the same. In this course we will use the programming language R as the primary tool for analysis.
Learning Outcomes By the end of the course:Students will be able to:1. Deploy a structured lifecycle approach to data science and big data analytics projects
2. Select visualization techniques and tools to analyze big data and create statistical models
3. Use tools such as R and RStudio, and MapReduce/Hadoop/Scala/Spark and Hadoop applications.
Module 1: Introduction to Data Analytics
Use Case of Data Analytics
· Introduction to Data Analytics
· Understanding your Data
· Clarifications of jargons such as Data Mining, Business Analytics, Functional Analytics, Machine Learning, Artificial Intelligence and the likes of Data Science Facilitating good analytical thinking with data visualization
· Installation of Software for Python, R and creating AWS Cloud server
· Basics of Python and R programming
Module 2: Understanding your data
· Concepts of Experiments, Outcomes, Sample space, events and Probability Models
· Concepts of Conditional Probability, Independence and Bayes Theorem
· Concept of lift, Support, sensitivity and specificity and their illustrations in Business Analytics
· Random variables, Data generation process, sample and population, parameters and inferential problems
· Distribution Functions
· Markov Chains and usage
· Simulation concepts
Module 3: Introduction to R
Exploratory Data Analysis with R
· Loading, querying and manipulating data in R
· Cleaning raw data for modelling
· Reducing dimensions with Principal Component Analysis
· Extending R with user–defined packages
Facilitating good analytical thinking with data visualization
· Investigating characteristics of a data set through visualization
· Charting data distributions with boxplots, histograms and density plots
· Identifying outliers in data
Module 4: Working with Unstructured Data working with Python
Mining unstructured data for business applications
· Preprocessing unstructured data in preparation for deeper analysis
· Describing a corpus of documents with a term–document matrix
· Make predictions from textual data
· Text Analytics
Module 5: Predicting Outcomes with Regression Techniques using Advanced Excel, Python and Knime
Estimating future values with linear regression
· Modelling the numeric relationship between an output variable and several input variables
· Correctly interpreting coefficients of continuous data
· Assess your regression models for ‘goodness of fit’
Module 6: Categorizing Data with Classification Techniques using Knime, Python and R
Automating the labelling of new data items
· Predicting target values using Decision Trees
· Constructing training and test data sets for predictive model building
· Dealing with issues of overfitting
Assessing model performance
· Evaluating classifiers with confusion matrices
· Calculating a model’s error rate
Module 7: Detecting Patterns in Complex Data with Clustering and Social Network Analysis
Identifying previously unknown groupings within a data set
· Segmenting the customer market with the K–Means algorithm
· Defining similarity with appropriate distance measures
· Constructing tree–like clusters with hierarchical clustering
· Clustering text documents and tweets to aid understanding
Discovering connections with Link Analysis
· Capturing important connections with Social Network Analysis
· Exploring how social networks results are used in marketing
Module 8: Leveraging Transaction Data to Yield Recommendations and Association Rules
Building and evaluating association rules
· Capturing true customer preferences in transaction data to enhance customer experience
· Calculating support, confidence and lift to distinguish "good" rules from "bad" rules
· Differentiating actionable, trivial and inexplicable rules
Constructing recommendation engines
· Cross–selling, up–selling and substitution as motivations
· Leveraging recommendations based on collaborative filtering
Module 9: Learning from Data Examples with Deep Learning
Deep Learning intricacies
· Learning the weight of a neuron
· Learning about how neural networks are being applied to object recognition, image segmentation, human motion and language modelling
· Analyzing labelled data examples to find patterns in those examples that consistently correlate with particular labels for object recognition
Module 10: Implementing Analytics within Your Organization
Expanding analytic capabilities
· Breaking down Data Analytics into manageable steps
· Integrating analytics into current business processes
· Reviewing Hadoop, Spark, and Azure services for machine learning
Dissemination and Data Science policies
· Examining ethical questions of privacy in Data Science
· Disseminating results to different types of stakeholders
· Visualizing data to tell a story
Data Analytics Bootcamp Tools (Skills normally picked up by trainees during the Training)
ETL Tools exposure
Data Bases and Data warehousing – MySQL and Teradata Basics
Machine Learning Skills, Deep Learning
Amazon Web Services – Big Data Analytics (Overview of Hadoop and its Projects)
R and its libraries
Python and its libraries
Data Visualization Tools - QlikView
Predictive analytics tools
Text Analytics Tools – Beautiful Soup
Social Media Analytics Tools
Web Scrapping tools… and lot’s more…
Data Analytics is a science!
More specifically the science of analyzing data, generating insights, and making predictions. It easily finds applications in social media analysis, computational biology, climate modeling, banking, insurance, market analytics, hospitality, health care, traffic monitoring and so on.
This course aims to provide to the Corporate and Individual learners an indepth study of advanced machine learning, data mining and statistical techniques that arise in real data analytic applications.
This course includes a detailed dive down into Predictive Analytics and Prescriptive Analytics. You will learn Ensemble techniques, Support Vector Machines, Time Series, Supervised and Unsupervised Learning Algorithms, Artificial Neural Networks and Clustering.
You will be installing and using confidently many tools Python, R, R Libraries, Azure ML, Amazon ML, Knime, Octave and visualization tools and databases.
This course is a Tri modal delivery course consisting of three aspects for corporates:
1. Precourse preparation Webinars
2. In classroom sessions (at your office)
3. Projects and Networking
For Individual Learners - the course is completely Online. We have special weekend batches for professionals and students. Write to firstname.lastname@example.org to enquire about the next batch.
The next batch starts on 9th Dec 2018
Enroll now to avail early bird discounts
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