Advanced Professional Certificate in Business Analytics with R&D

Advanced Professional Certificate in Business Analytics with R&D

Advanced Professional Certificate in Business Analytics with R&D

Course Duration: 6 months

(3 months Professional Training + 3 months Internship)

INTERNSHIP Placement GUARANTEED at UK-based Organisations

Tutorial Hours: 50

Research supervision hours: 20

Assessment methods:

1. Portfolio report: A portfolio is a systematic collection of student work that represents student activities, accomplishments, and achievements over a specific period of time in one or more areas of the curriculum.

2. Capstone Project based on Research & Development (R&D) [by Internship]: It is typically a final project that allows students to showcase the knowledge and skills they have gained throughout their academic program and apply them to real-world problems and issues.

CERTIFICATE

1. Certificate awarded by PPA Business School, France (https://www.ppa.fr/)

2. Certificate awarded by Brit College of Engineering & Technology (BCET), London (https://bcet.uk)

A SAMPLE CERTIFICATE >> AVAILABLE on REQUEST

Course Fees: £600(GBP)

Unlock Data Insights and Career Success

Business data analytics is the process of examining data and deriving insights to make informed business decisions. The primary objective of business data analytics is to identify patterns, trends, and insights in data that can help organizations make data-driven decisions. 

About the Business Data Analytics:

Pursuing an Advanced Course in Business Data Analytics opens doors to a variety of job opportunities in a rapidly evolving field. By acquiring essential skills and knowledge, participants can position themselves for success and drive impactful change in organizations through data analytics. As businesses continue to recognize the value of data, the prospects for skilled professionals in this area remain robust.

Goals & Objectives:

The primary goals of a Business Data Analytics Advanced Course include:

  1. Skill Development: Equip participants with essential analytical skills such as statistical analysis, data visualization, and predictive modeling.
  2. Tool Proficiency: Familiarize students with industry-standard tools like MS Excel, SQL, Python, R, Power BI, and Tableau.
  3. Strategic Thinking: Enhance the ability to make data-driven business decisions and provide actionable insights.
  4. Real-World Applications: Provide hands-on experience through case studies and projects that reflect current industry challenges.

Expected Outcomes:

Upon completion of an Advanced Course in Business Data Analytics, participants can expect to achieve the following outcomes:

» Proficiency in analyzing large datasets and deriving meaningful insights.

» Enhanced problem-solving skills tailored for business contexts.

» Ability to communicate analytical findings effectively to stakeholders.

» A robust portfolio demonstrating real-world applications of data analytics techniques.

Job Prospects & Career Path: 

With the growth of big data, job prospects for Business Data Analytics professionals are promising. Here is a table outlining some common job titles, descriptions, and average salaries:

Job Description

Average Salary (USD)

Data

Analyst

Analyzes data sets to identify trends and patterns, and provides reports.

$70,000 - $90,000

Business Intelligence Analyst

Utilizes data analysis tools to support decision-making in organizations.

 

$80,000 - $100,000

Data

Scientist

Applies statistical methods and algorithms to analyze complex data.

$100,000 - $130,000

Data

Engineer

Designs and manages data infrastructure and data pipelines for analysis.

 

$90,000 - $120,000

Analytics Consultant

Advises companies on how to leverage data for strategic advantage.

$85,000 - $115,000

 

At a Glance, Different Career Path:

Here are some common job titles you can achieve with such skills:

  • Data Analyst: Responsible for analyzing data, identifying trends, and presenting insights.
  • Business Intelligence (BI) Analyst: Focuses on transforming data into actionable insights using visualization tools.
  • Data Scientist: Applies advanced statistical and machine learning techniques to analyze data and build models.
  • Financial Analyst: Utilizes data analysis to support investment decisions and financial planning.
  • Marketing Analyst: Analyzes marketing data to optimize campaigns and improve customer engagement.
  • Operations Analyst: Improves operational efficiency through data analysis.
  • Database Analyst / Administrator: Uses SQL skills to manage and query the organization’s databases.

 

Key Job Responsibilities:

» Collecting and organizing data.

» Performing data cleansing and validation.

» Analyzing data using statistical methods and tools.

» Creating data visualizations and dashboards.

» Identifying trends and patterns.

» Presenting data insights to stakeholders.

» Developing data-driven recommendations.

» Automating data tasks.

» Collaborating with team members across different departments.

Salary Expectations: 

Salary can vary significantly based on experience, location, and industry. Here’s a general overview:

  • Entry-Level (0-2 years experience): $50,000 - $70,000 per year.
  • Mid-Level (2-5 years experience): $70,000 - $100,000 per year.
  • Senior-Level (5+ years experience): $100,000 - $150,000+ per year.

 

Job Factors affecting salary can be the following:

» Level of education (Masters degree could fetch higher pay).

» Specializations such as AI/Machine Learning.

» Specific Industry and location.

» Years of practical experience.

Why You Should Learn Business Data Analytics: 

Here are the compelling reasons why investing time in learning business data analytics is a wise choice:

  • High Demand: The shortage of skilled data analysts ensures strong job security and career growth.
  • Competitive Salaries: Data professionals earn above-average salaries.
  • Diverse Career Paths: Data analytics skills can lead to various roles across different industries.
  • Problem-Solving: Data analysts solve complex problems and make a real impact.
  • Personal Development: Data literacy is an increasingly important skill in all areas of life.

 

Skills Required: 

  • Analytical Thinking: Ability to think critically and logically.
  • Problem-Solving: Aptitude for identifying issues and developing solutions.
  • Statistical Knowledge: Understanding of statistical concepts and methods.
  • Technical Proficiency: Expertise in data analysis tools (SQL, Python, R, etc.).
  • Communication Skills: Ability to clearly present data insights.
  • Attention to Detail: Careful and precise in data handling and analysis.
  • Business Acumen: Understanding of business processes and objectives.

 

Why Data Analytics: 

Business Data Analytics involves using data to make informed decisions, optimize processes, and drive business growth. It encompasses several key stages:

  1. Data Collection: Gathering data from diverse sources.
  2. Data Cleaning & Preparation: Ensuring data accuracy and consistency.
  3. Data Analysis: Exploring and interpreting data using statistical techniques and tools.
  4. Data Visualization: Presenting findings in a clear and compelling manner.
  5. Data Reporting: Communicating insights and recommendations to stakeholders.

Key Tools Used in Business Data Analytics:

To navigate these stages successfully, data analysts rely on a suite of powerful tools. Let's explore the major players:

(a). MS Excel

MS Excel is a spreadsheet program developed by Microsoft. It is a widely used tool in business data analytics due to its ease of use and versatility. MS Excel has features such as pivot tables, data visualization, and statistical functions that enable data analysis.

(b). SQL

SQL (Structured Query Language) is a programming language used for managing and manipulating relational databases. SQL is used for querying, updating, and managing data in databases. It is a vital skill for data analysts and business intelligence professionals.

(c). Python

Python is a high-level programming language used in various applications, including web development, data science, and machine learning. Python has libraries such as Pandas, NumPy, and Matplotlib that enable data manipulation, analysis, and visualization.

(d). R

R is a programming language used for statistical analysis and visualization. R has libraries such as ggplot2 and dplyr that enable data manipulation, analysis, and visualization. It is a popular tool in data science and statistical analysis.

(e). Power BI

Power BI is a business analytics tool developed by Microsoft. It provides interactive visualizations and business intelligence capabilities, enabling users to create reports and dashboards. Power BI has features such as data modeling, data transformation, and data visualization.

(f). Tableau

Tableau is a data visualization tool used for creating interactive dashboards and reports. It has features such as data blending, data transformation, and data visualization. Tableau is a popular tool in business data analytics due to its ease of use and powerful visualization capabilities.

Key Syllabus Overview:

A comprehensive syllabus for learning business data analytics using these tools would typically cover the following areas: 

Module

Topics Covered

Tools Focus

Fundamentals

Introduction to Data Analytics, Data Types, Data Sources, Data Collection, Ethical Considerations

General

Concepts

MySQL Server installation and Workbench

Understand basic SQL syntax and how to write queries.

Learn database operations like SELECT, INSERT, UPDATE, and DELETE.

Work with database and table structures using CREATE, DROP, and ALTER.

Understand advanced SQL operations like JOINs, UNION, and GROUP BY.

Handle NULL values and learn about constraints.

Use SQL functions for strings, numbers, and dates.

Create and manage indexes and views.

Work with advanced SQL features like wildcards and aliases.

MySQL

SQL Server installation

Understand what SQL is and why you would use it.

Create and manage databases and tables.

Insert, update, and delete data in tables.

Use SELECT with filters (WHERE) and sorting (ORDER BY) to get data.

Use JOIN to combine data from multiple tables.

Use functions like SUM, AVG, and COUNT to group data and add them up.

Create table relationships with foreign keys.

Use SQL functions like MIN, MAX, and COUNT.

Understand subqueries and indexes.

Manage user permissions and data security.

MS SQL Server

Data

Management I

Data Entry, Data Validation, Data Cleaning, Sorting & Filtering, Data Transformation, Data Preparation, Basic Statistics, Pivot Table, Creating Business Intelligence Environment by EXCEL

MS Excel

Data

Management II

Database Concepts, Relational Database Design, Introduction to SQL, Data Definition Language (DDL), Data Manipulation Language (DML), Querying

SQL

Programming with Data

Python Basics, Data Structures, Pandas Library for Data Manipulation, NumPy for Numerical Operations, Basic Data Visualization with Matplotlib, Machine Learning, Deep Learning, Modern Machine Learning Web App with Streamlit

Python

Advanced

Statistics

Statistical Analysis using R, Data Modelling, Hypothesis Testing, Regression Analysis, Time Series Analysis

R

Data

Visualization I

Creating basic charts and graphs, Dashboard design principles, Data storytelling, Interactive dashboards, Data Analysis Expressions (DAX)

 

 

Power BI

Data

Visualization II

Advanced visualizations, Working with different data sources, Interactive Dashboards, Data Exploration, Customization

Tableau

Capstone Project

Real-world project involving the entire data analysis process, from data collection to presentation

And R & D

 R & D and  Application  of the above  tools

 

Research & Development (R&D):

(DURING INTERNSHIP)

Research Methods – Implementation & Evaluation

Big Data Analytics and Tools

Data warehousing

Data Analytics for Enterprise Systems (ES) – Tools, Techniques, & Methods

Cloud platform for Data Analysis

Google Cloud Platform (GCP), Amazon Web Services (AWS), Snowflake, and Microsoft Azure.

Tableau to SQL Server / MySQL on AWS (Amazon Web Services)

Power BI to SQL Server / MySQL on AWS (Amazon Web Services)

SQL-like business intelligence applications

Data Management Strategy with Data Warehouse and Data Lake

Lakehouse Top Four: AWS, Snowflake, Azure & Google Cloud

Data-driven decision-making

Applications in Emerging Technologies (AI, Machine Learning, IoT, Blockchain, Cloud Computing, Digital Twin, Metaverse, AR/VR/MR/XR, etc.)

Data Science and Data Engineering Tools

Frequently Asked Questions (FAQs)

1. What are the prerequisites for joining an Advanced Course in Business Data Analytics?

Most programs require participants to have a basic understanding of statistics and familiarity with spreadsheets. A background in Business, Engineering, Healthcare, Applied Science or IT can be beneficial.

2. How long does it typically take to complete the Advanced Course?

Programs usually range from a few weeks to several months, depending on the curriculum and format (full-time, part-time, or online).

3. Are there any certifications awarded upon completion?

Yes, participants often receive a certificate from the Brit College of Engineering and Technology (BCET, https://bcet.uk) London that can enhance their credentials when applying for jobs in the analytics field.

4. What industries can I work in after completing this Advanced Course?

Graduates can find opportunities across various sectors, including finance, technology, manufacturing, healthcare, marketing, consulting, and e-commerce.

5. How can I leverage my Advanced Course experience in job applications?

Highlight relevant projects, tools used, and specific analytical skills developed during the program on your resume and during interviews.

 

Apply Now