Data Analytics Certification Program

Ritetrac’s Data Analytics Certification Program provides a range of big data training designed to help participants and their
organizations use new and existing internal resources to make the most of key data science tools and platforms.
Data Analytics courses are designed to deliver the basic requirement for any data professional and big data analysts to make
business impact. The courses cover core data analytics programming tools and applications to entrench the necessary background knowledge.
The training sessions are delivered online. Training course participants will first be taken through Microsoft Excel Proficiency and necessary tools will be installed on the participants laptop in advance of any of the courses.
Ritetrac D&A programs offer:

1. Hands-on, online training.
2. A highly consultative engagement. There will be plenty of time to discuss your specific projects and learning objective to provide
   immediate return on investments upon completion of any of the programs and professional certification.

Course Overview
In this complex, digital world, clients want helps to understand their data to drive greater insight, improved performance and
competitiveness. The course will introduce participants to the important techniques and methods to become more efficient in delivering their daily objectives and also improve their work ethics.

Becoming a senior data analyst takes more than the understanding of basic skills like statistics and programming in various
languages. The need to develop one area of technical analytic expertise (e.g., machine learning), while being conversant in many
others is very critical. This is the major objective of this course.
By the end of this intermediate data science course, you’ll be ready to:

• Build data solutions that integrate with other systems.
• Implement advanced data analytics concepts like machine learning and inferential statistics to address critical business problems
  and influence corporate decision making.
• Participate successfully in data science competitions.
• Going beyond descriptive analytics has become essential to meet the complexities of information requirement for
• decision making as well as developing strategies to drive greater profitability, improved performance and competitiveness. The course builds expertise in advanced analytics, data mining, predictive modeling, quantitative reasoning and web analytics, as well as advanced communication and leadership.
• Ritetrac’s Data & Analytics Certification Program in advanced and predictive analytics covers the following:
• Articulate analytics as a core strategy
• Transform data into actionable insights.
• Develop statistically sound and robust analytic solutions.
• Evaluate constraints on the use of data.
• Assess data structure and data lifecycle.
  This course integrates data science, information technology and business applications into three areas: data
  mining, predictive (forecasting) and prescriptive (optimization and simulation) analytics.

Ritetrac’s Data & Analytics certification course for beginners, is designed for:

• Graduate Trainees
• Data Analysts
• Business Analysts
• Procurement and supply chain professionals
• Warehouse and Material Management professionals
• Professionals looking to change career path and etc
  This course delivers the basic requirement for any aspiring data analyst and big data analysts to make business impact in few months. The course covers the core concepts of analytics and reporting with introduction to the use of a visualization tool (often PowerBI) to entrench the necessary background knowledge.
• At the end of this course, participants will have a basic understanding of how each of these methods learn from
• data to find underlying patterns useful for prediction, classification, and exploratory data analysis.
• Further, each participant will learn the implementation of machine learning methods in the R statistical
• programming language for improved decision-making in real business situations.
  Course Outline
• Week 1:
• Introduction to Data Analytics
• Data Analytics Fundamentals I
• Introduction to Visualization
• Test/Assessment
• Week 2:

Exploratory Data Analysis/Visualization

• Data Visualization / Dashboarding Fundamentals
• Practical data Visualization using PowerBI /Tableau
• Visualization / Dashboarding Case Study I
• Test/Assessment
• Week 3:
• Intermediate Data Analytics for Beginners
• Data Visualization/ Dashboarding for
  Enterprise Reporting
• Visualization / Dashboarding Case Study II
• Test/Assessment

Week 4:

• Introduction to Data Analytics
• Data Analytics Fundamentals I
• Introduction to R
• Test/Assessment
• Week 5:
• Exploratory Data Analysis/Visualization
• Introduction to Visualization
• Practical data Visualization using Power BI
• Introduction to SQL
• Week 6:
• Intermediate Data Analytics for Beginners
• Data Analytics fundamentals II
• Introduction to modelling
• Test/Assessment
• Week 7:
• Advanced Analytics for Beginners
• Linear Regression
• Logistic Regression
• Model Diagnostics
• Week 8:
• Introduction to Time Series Modelling/forecasting
• Beginners course personal project/case study
• Intermediate Level:
• Week 1:
• Data Wrangling
• Data in Databases: Get an overview of relational
  and NoSQL databases and practice data.
  manipulation with SQL.
• Introduction to Data Visualization
  using PowerBI /Tableau/Qlik Sense
• Review of Statistical Methods
• Week 2
• Inferential Statistics • Data Science Fundamentals II 2 • Theory and application of inferential statistics • Parameter estimation • Hypothesis testing • Introduction to A/B Testing
• Week 3:
• Predictive Analytics I – Linear Modelling • Linear Algebra Overview 3 • Exploratory Data Analysis • Linear Regression • Multiple Linear Regression • Regression Diagnostics • Logistic Regression • Statistics Assessments
• Week 4:
• Predictive Analytics II – Machine Learning • Scikit-learn 4
• Supervised and unsupervised learning • Random Forest, SVM, clustering • Dimensionality reduction • Validation & evaluation of ML methods
• Week 5:
• Introduction to Advanced Analytics
• Techniques
• Text Mining
• Simulation of sentimental analysis
• Introduction to Optimization – Causal and
  Mechanistic Analytics
• Time Series and Forecasting
• Guided Project
• Advanced and Predictive Analysis
• Week 1:
• Math for Modelers
• Techniques for building and interpreting mathematical/statistical models of real-world phenomena in and across
• multiple disciplines, including matrices, linear programming and probability with an emphasis on applications will be
• covered.
• This is for participants who want a firm understanding and/or review of these fields of mathematics/statistics prior to
• applying them in subsequent topics.
• Introduction to Statistical Methods
• Participants will learn to apply statistical techniques to the processing and interpretation of data from
• various industries and disciplines.
• Topics covered include probability, descriptive statistics, study design and linear regression. Emphasis will be placed
• on the application of the data across these industries and disciplines and serve as a core thought process through
• the entire Predictive Analytics curriculum.
• Data Preparation
• In this course, Participants explore the fundamentals of data management and data preparation. Participants acquire
• hands-on experience with various data file formats, working with quantitative data and text, relational (SQL)
• database systems, and NoSQL database systems.
• They access, organize, clean, prepare, transform, and explore data, using database shells, query and
• scripting languages, and analytical software.
• Week 2:
• Generalized Linear Models
• This extends Regression and Multi Analysis by introducing the concept of Generalized Linear Model “GLM”. Reviews
• the traditional linear regression as a special case of GLM’s, and then continues with logistic regression, poisson
• regression, and survival analysis.
• It is heavily weighted towards practical application with large data sets containing missing values and outliers. It
• addresses issues of data preparation, model development, model validation, and model deployment.
• Intro to Advanced and Predictive Analytics – Regression and Multivariate Analysis
• This introduces the concept of advanced and predictive analytics, which combines business strategy,
• information technology, and statistical modeling methods. The course review s the benefits of analytics,
• organizational and implementation/ethical issues.
• It develops the foundations of predictive modeling by introducing the conceptual foundations of regression and
• multivariate analysis; developing statistical modeling as a process that includes exploratory data analysis,
• model identification, and model validation; and discussing the difference between the uses of statistical models
• for statistical inference versus predictive modeling.
• The high-level topics covered in the course include exploratory data analysis, statistical graphics, linear regression,
• automated variable selection, principal components analysis, exploratory factor analysis, and cluster analysis.
• In addition, Participants will be introduced to the R statistical package, and its use in data management and
• statistical modeling.
• Prerequisite: Introduction to Statistical Methods.
• Week 3:
• Time Series Analysis and Forecasting
• This covers key analytical techniques used in the analysis and forecasting of time series data.
• Reviews the role of forecasting in organizations, exponential smoothing methods, stationary and nonstationary time
• series, autocorrelation and partial autocorrelation functions, Univariate ARIMA models, seasonal models, Box-Jenkins
• methodology, Regression Models with ARIMA errors, Transfer Function modeling, Intervention Analysis, and multivariate
• time series analysis.
• Prerequisite: Generalized Linear Models
• Week 3:
• Machine Learning Techniques
• In this course, several practical approaches to machine learning methods with business applications in
• marketing, finance, and other areas are covered.
• The objective of this is to provide a practical survey of modern machine learning techniques that can be applied
• to make informed business decisions:
• Regression and classification methods
• Resampling methods and model selection
• Tree-based methods
• Support vector machines and kernel methods
• Principal components analysis and
• Clustering methods
• Week 4:
• Machine Learning Techniques
• In this course, several practical approaches to machine learning methods with business applications in
• marketing, finance, and other areas are covered.
• The objective of this is to provide a practical survey of modern machine learning techniques that can be applied
• to make informed business decisions:
• Regression and classification methods
• Resampling methods and model selection
• Tree-based methods
• Support vector machines and kernel methods
• Principal components analysis and
• Clustering methods
• Week 5:
• Certification Examination/ Project

Training Dates:

May 1st – September 2nd 2024

Mode of Training Delivery:

Online Instructor Led Google Meet /Zoom/Microsoft Teams}

Training Cost:

4,925,000 NGN

Bank Details:

Bank: Guaranty Trust Bank Plc

Account No: 0023331797

Account Name: Ritetrac Consulting Nigeria Ltd.

Address:

Office Addresses:
Suite A8, Abuja Shopping Mall, Wuse Zone 3 Federal Capital Territory, Abuja
info@ritetracconsult.com.ng
www.ritetracconsulting.com.ng
+234-8038769323, +234-8037090474