Predictive Analytics Coaching

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Switching to Big Data Career Coaching Program

Why this program?

At this point and time there aren’t any formal training programs at our local universities on Big Data. For aspiring professions who can’t afford to go abroad and study, they are left with no option but to self learn. This course is for people who want to develop or advance their careers in the data space without having to travel abroad to learn. It’s also for people who’d like to become independent consultants or entrepreneurs within the data niche.

A Little About our career journeys

In this course we have documented exactly what steps we took to achieve this outcome. We share tips for overcoming hurdles that you may be forced to overcome while providing shortcuts to show you all the people, places, and things that really matter; in hopes that you won’t have to waste time exploring some of the fruitless options like we did. We will also give you access to online learning resources from affiliates and you will not be charged anything extra. Along the way, and based on your skill competency, we will assign you paid projects from our lab to kick start your data-prenuership journey!

Why you should join Us

  1. One on one mentorship and coaching
  2. Facilitation by the best industry experts
  3. Learn the latest technologies
  4. Access to paid projects
  5. Job Placement

The Benefits of Getting Into Big Data

  1. Pay and Career Advancement Interesting
  2. Work High Demand/ Low Supply
  3. How Can You Improve Your Skills and Be in Demand?
  4. Industries Using Big Data

What You Should Know About Big Data?

  1. What Putting in Your Own Lawn Has to Do With Big Data
  2. Why Is Big Data So Much More Complicated?
  3. How Long Will It Take to Learn
  4. Changes You’ll Need to Make Not Just Beginner Skills
  5. Which Technology Should You Learn
  6. Future Proofing Your Career
  7. Is Big Data Going to Last?
  8. Retooling and Reskilling
  9. Newly Graduated
  10. General Advice on Switching Programmers and Software Engineers
  11. Managers
  12. Data Analysts and Business Intelligence
  13. DBAs and SQL-focused Positions
  14. Operations
  15. What if You’re Not on this List?

How Do You Get a Job?

  1. No Experience Necessary?
  2. Where Do You Fit in the Data Science Ecosystem?
  3. Personal Project
  4. Networking
  5. Getting a Job Fast(er)

What Are You Going to Do?

  1. Questions to Answer Before Starting to Learn Big Data
  2. Your Checklist For Starting to Learn Big Data
  3. Interview Questions for Big Data.
Introduction to Data Science

Python for Data Science

  1. Learn to use NumPy for Numerical Data
  2. Implement Machine Learning Algorithms
  3. Learn to use Pandas for Data Analysis

Python for Data Visualization

  1. Learn to use Matplotlib for Python Plotting
  2. Learn to use Seaborn for statistical plots
  3. Use Plotly for interactive dynamic visualizations

Python for Machine Learning

  1. Use SciKit-Learn for Machine Learning Tasks
  2. K-Means Clustering
  3. Logistic Regression
  4. Linear Regression
  5. Random Forest and Decision Trees
  6. Natural Language Processing and Spam Filters

Use R for Data Science and Machine Learning

Data Engineering Training Bootcamp

Who is a Data Engineer?

A Data Engineer is someone with specialized skills in creating software solutions around data. Their skills are predominantly based around Hadoop, Spark, and the open source Big Data ecosystem projects. Data Engineers come from a Software Engineering background and program in Java, Scala, or Python.

A Data Engineer has realized the need to go from being a general Software Engineer and specialize in Big Data as a Data Engineer. This is because Big Data is changing and they need to keep up with the changes. Also, there is a copious amount of knowledge that a Data Engineer needs to know and there isn’t enough time to keep up with Big Data and other general software topics.

A qualified Data Engineer’s value is to know the right tool for the job. They understand the subtle differences in use cases and between technologies, and they can create data pipelines. This course will take you the right skills for the job.

Introduction to Hadoop

  1. Understanding Big data
  2. Distributed Hadoop architecture overview
  3. Hadoop releases and Ecosystem Overview

Hadoop Architecture and Concepts

  1. Mapr and Apache Hadoop architectural concepts
  2. HDFS/Mapr red and write
  3. HDFS commands


  1. Introduction MapReduce
  2. MapReduce program

Introduction to Hadoop Ecosystem

  1. Introduction to Spark
  2. Writing and running spark job
  3. Introduction to hive/impala
  4. Introduction to Sqoop
  5. Introduction to Drill (SQL Querying engine)
  6. Cassandra Unstructured Key - value pair data storage
  7. Hbase unstructured key-value pair storage
  8. Introduction to Kafka and Flume

Advanced Spark Concepts

  1. Reading files with spark
  2. Reading and writing to Cassandra table
  3. Reading and writing to Hbase table
  4. Spark Streaming concepts

Data Ingestions

  1. Introduction to Streamsets
  2. Introductions to Data ingestions using Zeppelin

Data Analytics & Visualization

  1. Introductions to Python for data analytics
  2. Introductions to data visualization using Zeppelin
  3. Introduction to Grafana, Elasticsearch and Kibana
Data Visualization Training Boot Camp

What is Data Visualization?

Data visualization is vital in bridging the gap between data and decisions. Data visualization is an important visual method for effective communication and analyzing large datasets. Through data visualizations we are able to draw conclusions from data that are sometimes not immediately obvious and interact with the data in an entirely different way.

The 3 month training course whose aim is to provide participants with an accessible, broad and deep understanding of data visualization and infographic design. This course will provide you with an informative introduction to the methods, tools and processes involved in visualizing big data. The focus of the training is to teach the craft of this discipline, helping participants to know what to think, when to think about and how to resolve all the analytical and design decisions involved in any data-driven challenge.

Who should attend?

We have designed the course for people from different fields who want to learn how to produce visualization’s that help us better understand real-world big data problems. You might be a Journalist, Analyst, Graphic Designer, Managers, Finance, Statistician, or Researcher looking to enhance the creativity and impact of your communications. Perhaps you possess a creative flair, as a designer or developer, and you’re seeking to enhance the rigor of your data-driven capabilities? Maybe you do not personally get involved in the analysis or presenting of data but coordinate others who do.

Whether we realize it, we are all frequent consumers of visualization and infographic designs so improving the sophistication of how one reads, interprets and evaluates the effectiveness of such displays is a key literacy. You will gain the most from the practical exercises if you are comfortable with computer programming however you don’t need to have any prior experience using the software listed below.

We will use a variety of tools so that you become comfortable engaging with different software and confident trialing new packages to find those that best meet your needs.

Tools to be used

  1. Python
  2. Javascript
  3. D3.js
  4. Data Hero
  5. Google Charts

Topics Covered

  1. Defining data visualization and infographic design.
  2. Overview of a workflow driven approach for efficiency and effectiveness The key principles of good data visualization design.
  3. The importance of developing visualization literacy for effective reading
  4. Developing an eye for critical evaluation.The influence of contextual factors
  5. Assessing the physicality and meaning of your data.Using visual techniques to explore data.The components of editorial thinking.
  6. The data visualization design anatomy.
  7. The building blocks of data encoding.
  8. The spectrum of different chart types and their roles.The features and role of interactivity in visualization design.
  9. The role of effective annotation for assistance and insight.
  10. Colour theory and best practice applications.The architectural considerations of composing a visualization work
  11. Data Visualization Tools
Algorithmic Marketing Training Boot Camp

What is Algorithmic Marketing?

We define algorithmic marketing as a marketing process that is automated to such a degree that it can be steered by setting a business objective in a marketing software system. This implies that the marketing system should be intelligent and knowledgeable enough to understand a high-level objective, such as the acquisition of new customers or revenue maximization, to plan and execute a sequence of business actions, such as an advertisement campaign or price adjustment, with the aim of achieving the objective, and to learn from the results to correct and optimize the actions if needed. In this course, we also use the term programmatic to refer to highly automated marketing software systems and services, and the terms algorithmic and programmatic are used interchangeably in most contexts.

Who should attend?

Adverting Agency Executives, Media Planners, Media Buyers, Implementers of marketing software, Marketing Managers, Product managers, Finance managers and software engineers who want to learn about the features and techniques that can be used in marketing software products and also learn about the economic foundations for these techniques.

The other target audience are Market Research Executives, Media Research, Marketing strategists and Technology leaders who are looking for guidance on how marketing organizations and marketing services can benefit from machine learning and Big Data and how modern enterprises can leverage advanced decision automation methods.

Tools to be used

  1. Python
  2. GIS

Machine Learning and Predictive Modelling Introduction

This training looks at basic methods of machine learning and economic modelling that enable predictive analysis and its building blocks. Focus will be to describe the main capabilities and limitations of predictive modelling, rather than to provide a comprehensive study of machine learning algorithms.

Promotions and Advertisements

This training focuses on targeting as a problem of customer experience optimization that is driven by a mix of multiple business objectives and controls many different marketing activities. It begins with an overview of the retail promotion environment that will help you to better understand the problem of targeting. It then describes a promotion targeting framework that includes a more formal definition of business objectives, basic building blocks of behavioural modelling, and more complex constructs used in marketing campaigns. After that, it looks at online advertising environment and related targeting methods. Although the retail and online advertising environments complement each other and many targeting methods are universally applicable, we will study them separately because of major structural differences and variations in objectives. Measurement plays an extremely important role in all marketing applications, and the framework that we develop will also be used in other programmatic services, including search, recommendations, and pricing.


In this training, we will look at first product discovery service which is search. The purpose of search services is to fetch offerings that are relevant to the customer’s search intent expressed in a search query or selected filters. Search services solve the problem of product discovery, which can be viewed as a particular case of targeting. We will be taking a practical approach to search methods and will focus on industrial experience, techniques, and examples, rather than information theory. At the same time, we will try to avoid implementation details, such as data indexing, as much as possible and will stay focused on the business value delivered through relevant search results. We will start this training with a review of the environment and economic objectives. We will then demonstrate that the problem of relevant search can be expressed in terms of features, signals, and controls, similarly to other programmatic services. After that, we will review a number of methods for engineering, mixing, and tuning these signals and controls in manual mode, and we will then discuss how predictive analytics can be leveraged for automated optimization.

Building Recommendations engines

Digital channels enable marketers to carry extremely wide and deep assortments with a large number of slow-moving niche products. This is one of the key differentiators in a comparison with traditional distribution channels, where the assortment is limited by distribution costs. Extremely wide and deep assortments with a long tail of niche products create a need for efficient discovery services, including search and recommendation. Recommendation services, in contrast to search services, aim to provide the customer with relevant offerings when a search intent is not or cannot be clearly expressed. Also, recommendation systems can generally leverage user–item interaction data, which include explicitly provided ratings and implicitly collected browsing histories, catalogue data, and contextual information. The main output of the recommendation service is a ranked list of recommended items Therefore, this training systematically describes recommender systems by starting with the environment and economic goals and then diving deeper into various recommendation methods.

Pricing and Assortment

The last part of this training we will look at the problem of price management. We will start with a review of the basic principles of strategic pricing and price optimization. We will then continue with the development of more tactical and practical demand prediction and price optimization methods for market segmentation, markdowns, and clearance sales. We will also briefly review the major resource allocation methods used in service industries to set booking limits. Finally, we will consider the assortment optimization problem, for which we can reuse some of the building blocks developed for price management.
Location Intelligence Training Boot Camp

What is Location Intelligence?

Location intelligence is more than analysis of geospatial information or geographic information systems alone, it is the capability to visualize spatial data to identify and analyze relationships. Evolving from GIS, location intelligence provides analytic and operational solutions across organizations.

Organizations have discovered that data can be one of the best ways to get insights about customers and how to serve them better, increasing brand loyalty and improving customer relationship management. Linking customer addresses to a geographic area and then running these against internal company data and external demographics such as census data and income data, or other open data can provide unprecedented levels of detail. Who people are, what they do, and how and when they consume is tied to the where, in essential ways. What is their neighborhood, commute, and workplace?

These locations and their spatial relationships lead to a more in-depth understanding of behavior and influences. Since a high percentage of data already has geographical information attached to it, insights about these relationships are readily available. Location intelligence now allows for incorporating external data from a variety of sources that can be combined and updated dynamically in the cloud. Companies can update the accessibility of their brand locations, marketing and potential new sites accordingly.

What you will learn

Learn how to map excel data, create data dashboards, and derive deep insights from location data. Discover how real companies across a range of industries and categories: finance, real estate, economic development and operational logistics are making use of location intelligence technology to gain a competitive edge in the marketplace.

Who should attend?

Researchers, Sales Managers, Marketing Managers, Customer Service Managers and software engineers who want to learn about the location features and techniques that can be used in marketing software products.

The other target audience are Market Research Executives, Research, Marketing strategists and Technology leaders who are looking for guidance on how organizations and marketing services can benefit from location intelligence.

Tools to be used?

  1. Python
  2. ArcGIS
  3. Google Maps

Introduction to ARCGIS

  1. Opening and saving a map document
  2. Working with map layers
  3. Navigating a map document ArcGIS and Measuring Distance
  4. Working with feature Attributes, attribute table and Labelling Features

GIS Map Design

  1. Creating point and Polygon Maps
  2. Creating custom classes for Maps
  3. Creating Density Maps and Fishnet Maps
  4. Creating custom colors for maps as well as

GIS Outputs

  1. Creating a Map layout
  2. Creating Legend
  3. Adding Excel or charts, Title, North, Scale bar

Basic Geodatabases

  1. Create a feature class and fill in Attributes
  2. Tour of Arc Catalogue
  3. Adding Excel or charts, Title, North, Scale bar Modify attribute table and Join Tables
  4. File Structure in Arc Catalogue

Introduction to Spatial Data

  1. Learn More about vector data and raster data
  2. Introduction to model builder for ARCGIS
  3. Geo Processing Spatial Analysis

Geo-processing Tools

  1. Clipping Feature
  2. Buffering Feature
  3. Dissolve Feature
  4. Intersect Feature
  5. Union Feature
  6. Merging Feature

Digitizing Features

  1. Polygon Digitization
  2. Line Digitization
  3. Point Digitization
  4. Advanced Editing toolbar
Speciality Training Areas (Select with relevance to your industry)

Transportation & Routing

  1. Network Analysis
  2. Location of Bus Stops and Zones
  3. Route Planning and analysis
  4. Surface Analysis for Roads and civil Structures
  5. Facilities to Destinations

Emergency Mapping and Response

  1. Fire Response
  2. Finding the optimum location for a fire station of police station
  3. Identify trends in crime patterns from data and predict
  4. Improve response time and mitigate

Real Estate Mapping and Development

  1. Houses and rental unit mapping
  2. Building geodatabase and integrate with census and house block data
  3. Mapping change in value property
  4. Tax mapping and areas of development
  5. Using tools to identify locations and define schemes
  6. Neighborhood mapping/ insurance companies

Maritime Mapping:

  1. Mapping of travel Routes
  2. Mapping of potential sea incidents
  3. Mapping of routes for goods

Retail and Consumer Stores:

  1. Location analytics
  2. Site location/customer location
  3. Market Analysis
  4. Expand markets

Tourism and travel

  1. Locations for camps and tourist attractions
  2. Identify routes based on wildlife data and estimations


  1. Surface analysis
  2. Land planning
  3. Agriculture
  4. Protected areas
  5. River flows and landform analysis
  6. Habitat analysis


  1. Mapping of utility networks
  2. Identify leaks and areas and how they will impact people
  3. Land use and hazard areas


  1. Public safety
  2. Rural and urban development
  3. Transit
  4. Security and planning
  5. Land Planning and Infrastructure
  6. Zoning census and data

Oil and Energy:

  1. Mapping of oil sites and well locations and data
  2. Identifying areas of mineral and oil deposits using past data and analysis
  3. Create a pipeline routes and cost effective methodologies
  4. Utilization of proposed sites for Energy sources such as wind solar.
  5. Geology

Data Collection ARCGIS & Zoning:

  1. Build geodatabases for data collection
  2. Collector for ArcGIS application integration with Desktop
  3. Operations Dashboard for real time data capture
  4. Data analysis and representation