CONTENT BLOCKS OF VBA's

APPLIED DATA SCIENCE PROGRAM

1.

FUNDAMENTALS OF ANALYTICS

5.

APPLIED DATA SCIENCE

9.

EXTRACTION TRANSFORM AND LOAD TO BUILD MODERN DATA PIPE

2.

MASTERING IN PYTHON

6.

APPLIED MACHINE LEARNING

10.

BUILDING ANALYTICS PLATFORM IN GCP

3.

STANDARD SQL

7.

APPLIED AI

11.

CLOUD BASICS

AWS & AZURE

4.

BUILDING MODERN DATA WAREHOUSE BUILDING IN BQ

8.

REAL-TIME DASHBOARD AND BI FUNCTIONS

12.

CAPSTONE PROJECT

01

fundamentals of analytics

  • Understanding analytics processes
  • Knowing about important roles and responsibilities as
    • data scientists
    • data engineers
    • business analysts
    • market analysts
    • research scientists
    • analytics engineers and so on
  • Developing foundation around
    • Analytics platform
    • business intelligence platform
    • AI platform
    • Data platform
  • Understanding End to  end perspective of Data Product
  • Cross industry  alignment of analytical use cases such as
    • Telco,
    • Retail
    • Information Technology
    • Finance and Banking
  • Modern data science professionals way of working
    • Cross functional team​
    • Agile principles
  • Importance of capacity building in  Taking End to end responsibilities of developing data products
  • Data professionals proposition in both the global and local market

02

Mastering in python

  • Know how to use Python Environment (week 1)
  • Know how to use Anaconda
    • Google colab for Python programming (week 1)
  • Introduction to Python 3 basics (week 1)
  • Understand Python script structure Know variables, naming, types, and operators (week 1)
  • Know strings, formatting, print (week 1)
  • Understand list, tuple, set, frozen set, dictionary (week 2)
  • Understand List comprehension (week 2)
  • Understand Dictionary comprehension (week 3)
  • Understand Control Structures: IF -Else, Loops (week 3)
  • Introduction to PANDA (week 4)
  • Importing dataset, creating data frame (week 4)
  • Know how to use Pandas in Data manipulation and analysis Understand Pandas Series and Data frames (week 4)
  • Know all on how to use NumPy and NumPy Arrays (week 5)
  • Understand Lambda function (week 5)
  • Understand the concept of Object in Python (week 5)
  • Know file manipulation/scripting (week 6)
  • Know error handling (week 6)
  • Know how to use Databases with Python (week 6)

03

04

STANDARD SQL &

BUILDING MODERN DATAWAREHOUSE INBQ

  • What is SQL?
  • Data Model - Understanding Data
  • Data Model - History of Data Model
  • Hierarchical, Network, Relational, Entity, Relational Semantic, NoSql
  • Data Model  - Relational Vs Transactional Model
  • SQL Statement Type
  • DML - Data Manipulation Language
  • DDL - Data Definition Language
  • DCL - Data Control Language
  • TCL - Transaction Control Language
  • Create Table, Physical Table, Temporary Table, Intermediary Table 
  • Table Vs Views
  • Adding Comments & Best Practices on coding
  • Retrieving Data with Select
  • Data Types
  • Filtering data with Where
  • Distinct and Case
  • Aggregate Functions
  • Count, SUM, Min, Max, Null & Data Type Handling
  • Filtering aggregate data with Having
  • Join Basics - 6 types of join, Inner Join, Right Join, Left Join, Full Join
  • Sub/Nested Queries
  • Window Ranking & Aggregation
  • Pivot & UnPivot
  • Large Complex Queries

05

06

07

Applied data science

applied machine learning

applied ai

  • Overview of Data Science in terms of Exploratory Data Analysis
  • Overview of Machine Learning Techniques in Data Science
  • Building foundations in AI
  • Applied ML-Supervised
    • e.g. Support Vector Machine,
    • Linear Regression,
    • Logistic Regression.
    • Decision Tree,
    • K-nearest neighbour algorithms 
  • Applied ML -Unsupervised
    • e.g. k-means
    • DBSCAN
  • Applied ML - Reinforcement:
    • Q-learning
  • Distributed ML Framework - Overview of Data Science Libraries in PySpark 
  • Understand basic SciKit ML program structure: preprocessing, Data Splitting, Modeling, fitting, Training model, evaluation of model, predicting, cross-validation, storing and reusing a stored model with the new dataset  ​
  • Model Evaluation (Classification Accuracy, Confusion Matrix, Area Under Cover (AUC), Logarithmic Loss, F-Measure, RMSE, Mean Absolute Error) 
  • Deep learning + AutoML

08

Real time dashboard and

end to end business intelligence functions

  • Know the basics of DataStudio
  • Know how to connect Data Source
    • Ex. Google Marketing Platform products, including Google Ads, Analytics, Display & Video 360, Search Ads 360
    • Google consumer products, such as Sheets, YouTube, and Search Console
    • Databases, including BigQuery, MySQL, and PostgreSQL
    • Flat files via CSV file upload and Google Cloud Storage
    • Social media platforms such as Facebook, Reddit, and Twitter
    • In total Know how to import data from different sources to Data Studio
  • Know how to transform in Datastudio
  • Know how to create Reports in DataStudio
    • Tell your data story with charts, including line, bar, and pie charts, geo maps, area and bubble graphs, paginated data tables, pivot tables, and more.
    • Make your reports interactive with viewer filters and date range controls. The data control turns any report into a flexible template report that anyone can use to see their own data.
    • Include links and clickable images to create product catalogs, video libraries, and other hyperlinked content.
    • Annotate and brand your reports with text and images.
    • Apply styles and color themes that make your data stories works of data visualization art.
    • In total Understand how to use and modify Visualizations in Reports
  • Know how to create Dashboard in DataStudio 
  • Understand Data Studio Security
  • Know how to create Analysis Services Database with Datastudio
  • Know how to use Analysis Services with Datastudio

09

Building modern data pipe to automate extraction, transform and load processes

  • Learn details about data ingress and egress process
  • Build  and manage end to end ETL processes
  • Understand the meaning of entities, attributes and relationships
  • Know how ETL works in business settings
  • Know how ETL and BQ schedulers function
  • Learn Key principles of Building Modern Data pipes in Airflow workflow manager¨
    • Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writing code that instantiates pipelines dynamically.
    • Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.
    • Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built into the core of Airflow using the powerful Jinja templating engine.
    • Scalable: Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
  • Modern way of data collection for analytics. Gathering data into a data platform.
  • Batch processing Vs Real-time processing. How to process data with scalable frameworks, such Spark.
  • Connecting batch processing flows into robust pipelines.
  • Real-time processing. Data processing with scalable stream processing frameworks.
  • Deployment. Deploying batch processing applications in production.

10

buildinG analytics platform

  • Functional layers of building modern AI platform
    • Data Lake​
    • Data Platform
    • End-to-End Data Product Deployment
    • End-to-End Business Intelligence Pipeline
  • Essential components of a data platforms
  • On the top services and applications to facilitate a data platform
  • Developing analytical products on GCP

11

CLOUD BASICS (GCP, AZURE, AWS)

  • Learn basics of AWS services for Data Science
  • AWS S3 Storage
  • RDS
  • Redshift and Glue
  • Amazon Quicksight
  • Know basic GCP services for Data Science 
  • Know Google storage 
  • Understand
    • VM instances,
    • Google AI,
    • Google Datastudio
  • Know basics Azure services for Data Analysis 
  • Know Azure data storage 
  • Understand
    • PowerBI

12

capstone project

building end-to-end data product

industry level prod development

  • End-to-end data science project development
    Industry focused project implementation using relevant domain specific dataset

Implementation:
Value Base Academy will offer capstone projects on four demanding areas as stated in the following from A to D. Students are free to select their own domain for the capstone project upon consultation with relevant instructors.

​Project A: Telecom
Project B: Sales & Marketing
Project C: Information Technology
Project D: Fintech, Finance and Marketing
Project E: Retail
Project F: Health Care
Project H: Media and Digital Marketing Agencies
Project I: Garments and Textile Industries

BEST Pricing Strategy

Your right is fully protected

 

You have right to get full refund if you are not happy with the program and decide to discontinue with us within two weeks of starting the program.

Male Student

Students & Freshers

As a future data science professional, we are giving students or fresh graduates he ultimate package and the best deal.

*Fresh graduates must have completed their education within the last 2 years.

BDT 50,000

or $600

40% off to our regular fees of $1000

Flexible option: Pay in 3 instalments

Professionals

Working
Professionals

Working professionals get ready to gain one industry recognized skill to further their career as a well-rounded data science professional.

Applicable for candidates with more than 2 years of working experience.

BDT 60,000

or $700

30% off to our regular fees of $1000

Flexible option: Pay in 3 instalments

Business Team

Industry

leaders

Are you the CxO's of a company or holding any leading position? We will take care of your vision by offering necessary competences to make your organisation data driven.

Hone your data analytical expertise and gain an overview the core pillars of applied data science.

BDT 70,000

or $800

20% off to our regular fees of $1000

Flexible option: Pay in 3 instalments