Program Overview

In the digital economy, being able to make decisions informed by data is not optional – it is essential. Organizations that make decisions based on data simply perform better.

The increasing importance of big data and data science encouraged us to introduce a program that trains the learners in data science with a statistical, computational, and programming focus. This premium Data Science course is an opportunity for graduates and working professionals from varied disciplines to develop advanced data science skills.
At Techmarcon, our goal is to help every individual develop into a dynamic, skilled data scientist, adept at working in a variety of settings, and capable of meeting the challenges of the industry globally. During the program, learners will get an in-depth understanding of Data Mining, Machine Learning, Programming in Python, Statistics, Multivariate Methods, Nonlinear Optimization, and more. Our subject-matter experts have curated the course curriculum with the Industry requirements in mind. By the end of this course, we will have a batch of competent, socially responsible, and continuously employable professionals.

Starts On

06 April 2025

Duration

3 Months

Fees

£ 2720

Programme Modules

Module I: Introduction to Data Science & its Environments

Data Science Introduction
Data Science Environments- Introduction to R and Python
Anaconda Python Distribution and IPython Basics
Built-in Structures, Functions and Files in Python
NumPy Basics and Introduction to Linear Algebra
Python Pandas Basics and its Data Structures

Module II: Data Extraction and Data Wrangling

Introduction to Gradient Descent
Data Extraction and Web Scrapping
Data Scrubbing or Data Cleansing
Data Integration and Reduction
Data Aggregation and String Manipulation

Module III: Data Exploration and Inferential Statistics

Data Exploration with Summary Statistics
Data Exploration with Visualization, Introduction to Matplotlib
Inferential Statistics and Hypothesis Testing, Python stats-model package

Module IV: Data Modelling with Supervised Machine Learning

Introduction to Machine Learning with scikit-learn
Introduction to Feature Selection and Extraction
Supervised Learning: Naïve Bayes and k-NN
Supervised Learning: SVM and SVR
Supervised Learning: Naïve Bayes and k-N
Regression Methods: Linear and Logistic Regressions, Regularization Techniques

Module V: Ensemble Learning, Deep Learning and Unsupervised Learning

Decision Trees & Random Forests, Ensemble Learning: Bagging & Boosting
Deep Learning and ANN with Tensor flow
Unsupervised Learning: Distance Measures, K-mean and Hierarchical/
Agglomerative Clustering

Module VI: Forecasting, NLP, Social Networks Analysis & Big Data Analytics

Time Series Forecasting Analysis with pasty
Introduction to Natural Language Processing with NLTK
Social Networks Analysis
Introduction to Big Data Analytics

Admission Process

Step 1: Complete the application form for the course.

Step 2: Our Admissions team will review and shortlist your profile.

Step 3: Pay the registration fee to secure your seat in the course.