Data Science Expert

Job Ready Training with the real scenarios and projects. Join our training, get trained, create a portfolio website, and get hired.

Course Program​

Data Science Expert

A complete comprehensive Data Science Expert training based on hiring companies’ requirements with a portfolio website. Whether you’re just starting or looking to enhance your existing skills, this training will provide a clear and structured path. The training includes guided and unguided projects with real-time scenarios as well.

Training Overview

The Data Science Expert is an instructor-led training course to master the skills needed for a Data Scientist job. This training program includes everything from scratch to experienced data scientist-level knowledge. The training consists of all essential skills, practices, assignments, and projects to become a proficient data scientist with resources from world-renowned companies.

Training Content

The training content given below is a brief. The detailed content and structure will be explained in the introduction session. This comprehensive Data Science Expert program trains you to work with core knowledge of the data science industry with project experience. The training covers Python, Version Control (Git), Data Structures & Algorithms, SQL, Mathematics, Statistics, Data Collection & Visualization, Machine Learning, Deep Learning, NLP, and Computer Vision.

Stage 1 | Key Concept of Data Science and Programming

01 | Fundamentals of Data and Visualization

Data and Visualization includes: types of data, pie chard & bar chart, histograms & line chart, scatter & bubble plot, and comparison among univeriate, bivariate, multivariate analysis.

02 | Python for Data Science

Python programming includes: python fundamentals, data structures, exception handling, functional programming, object-oriented programming, modules and packages, python standard library, and familiarity with data science libraries.

03 | Version Control (Git)

Git and Github essential concepts includes: setup and configuration, stagging, inspect and compare, branching, remote repositories, temporary commits and Github fork, pull request, code review.

04 | Data Structures & Algorithms

Data Structure and Algorithms Essential Concepts includes: big 0 notation, arrays & linked lists, stacks & queues, hash tables, trees & graphs, sorting algorithms, searching algorithms, string manipulation algorithms, and recursion.

Stage 2 | Advance Data Science Concepts

01 | SQL for Data Science

SQL for AI includes: basic operations, complex queries, views, stored procedures & functions, triggers & events, transactions, database design, indexes, security and permissions.

02 | Mathematics and Statistics

Math for AI essential concepts includes: 1. Linear algebra: vectors & matrices, matrix operations, eigenalues & eigenvectors, singular value decomposition. 2. Calculus: derivative & gradients, partial derivatives, chain rules, integrals 3. probability: probability distributions, Bayes’ theorem,random variables, expectation and variance.

Statistics for AI essential concepts includes: descriptive statistics (mean, median, mode, standard deviation), hypothesis testing, confidence intervals, and regression analysis.

03 | Data Collection and Visualization

Data Collection and Data Visualization are two essential stages in the data science process.

  1. Data collection is the process of gathering raw data for analysis which includes: Sources of Data, Data Collection Methods, and Tools for Data Collection.
  2. Data visualization is the graphical representation of data to help uncover patterns, trends, and insights which includes: Types of Data Visualizations, Data Visualization Best Practices, Tools for Data Visualization, and Examples of Visualizations

04 | Tools for Data Science

  1. Data Manipulation & Analysis Libraries: Pandas, NumPy
  2. Data Visualization: Matplotlib, Seaborn, Power BI
  3. Machine Learning & Deep Learning: Scikit-learn, TensorFlow, Pytorch

Stage 3 | Expert Data Science Concept

01 | Machine Learning

ML essential concepts include respectively: preprocessing, supervised learning, unsupervised learning, model selection, model training and evaluation, overfitting & underfitting

02 | Deep Learning

Deep Learning: neural network, forward propagation, backpropagation, building multilayer perception and special neural network architectures.

03 | Natural Language Processing (NLP)

NLP essential concepts include: Regex, Text presentation: Count vectorizer, TF-IDF, BOW, Word2Vec, Embeddings, Text classification: Naïve Bayes, and Fundamentals of Spacy & NLTP library.

04 | Computer Vision

Computer vision essential concepts include: Basic image processing techniques: Filtering, Edge Detection, Image Scaling, Rotation, Library to use: OpenCV, Convolutional Neural Networks (CNN), Data preprocessing, and augmentation.

Stage 4 | Getting Ready for Placement

01 | Working with Projects

There are eight projects to have experience with before you create a portfolio website for you. You will build about eight Data Science projects to experiment with.

02 | Creating the portfolio

You will build a website with all of your work to showcase the skills that you expert in. The hiring companies expect you to have a portfolio website to verify your work these days.

03 | Preparing for interviews

You will be having mock interviews before you start attending the real interviews. We will prepare you well to succeed in your interviews.

04 | Landing in a job

We work with different recruiters and online job boards to find a suitable post. Therefore you have a greater chance of getting the job you want.

Training Duration

Same curriculum. Two different durations to complete and land a job. Whether you are enrolling part-time or full-time you will be trained the same.

Full-Time

3 months

24 hours per week

Extended a month for job placement preparation

Part-Time

6 months

12 hours per week

Extended a month for job placement preparation

Training Fee

There are multiple options for paying your training fee. Please choose one of the options or contact us for a flexible one.

Option 01

Once-off

$2,600

Pay upfront: get a discount on your training fee.

Full price

$3,250

Discounted

20%

Option 02

Initial fee

$1,250

Pay an initial fee, followed by two installments.

Each installment

$800 (800*2)

Discounted

12.3%

Option 03

Initial fee

$1,000

Pay an initial fee, followed by two installments.

Each installment

$1,000 (1,000*2)

Discounted

7.6%

Work on Real World Projects that hiring companies are preferred

Building Chatbots

A chatbot is a software application designed to simulate conversation with users through text or voice interfaces.

Credit Card Fraud Detection

Credit card fraud detection is the process of identifying unauthorized or suspicious transactions made using credit cards. It aims to protect cardholders and financial institutions from financial losses caused by fraudulent activities.

Fake News Detection

Fake news detection refers to the process of identifying and classifying news articles, social media posts, or other forms of information as false, misleading, or deceptive. It aims to prevent the spread of misinformation by leveraging natural language processing (NLP), machine learning (ML), and other analytical methods.

Forest Fire Prediction

Forest fire prediction refers to the process of forecasting the likelihood, location, and severity of wildfires based on various environmental, meteorological, and human-related factors. The goal is to proactively mitigate risks, protect ecosystems, and safeguard human lives and property.

Classifying Breast Cancer

Breast cancer classification refers to the process of identifying and categorizing breast cancer into different types or stages based on various diagnostic features. This is crucial for early detection, accurate diagnosis, and effective treatment planning.

Driver Drowsiness Detection

Driver drowsiness detection refers to the use of technology to monitor and identify signs of fatigue or drowsiness in drivers, with the goal of preventing accidents caused by sleepiness or inattention. This system is essential in enhancing road safety by alerting drivers before they become dangerously fatigued, reducing the risk of accidents.

Exploratory Data Analysis

Exploratory Data Analysis (EDA) is a crucial step in the data analysis process where data scientists and analysts explore and summarize datasets to understand their main characteristics, often with the help of graphical representations. The goal of EDA is to uncover patterns, relationships, anomalies, and insights that can guide further analysis or predictive modeling.

Customer Churn Analysis

Customer Churn Analysis is the process of analyzing customer behavior to understand why customers are leaving and how to prevent this from happening. Churn analysis is crucial in industries like telecommunications, banking, subscription-based services, and retail, where retaining customers is essential for long-term business success.

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