Program Duration
6 Months
Training Hours
Morning Shift & Afternoon Shift


What is data science?


Data science is a crucial component of many companies and is one of the most contested subjects in IT circles. Data science is the study of data with the goal of gaining important business insights. It is the combination of principles and practices from fields like computer engineering, mathematics, artificial intelligence, and statistics that is the best multidisciplinary approach to analyzing large amounts of data.


Zoople, as one of the leading training institutes, offers the best data science training in Kerala. Data science uncovers actionable insights hidden in an organization’s data and experience as the outcome of specific subject matter expertise like math and statistics, machine learning, specialized programming, artificial intelligence, and advanced analytics, which are further used to guide decision-making and strategic planning.


Data Science Involves


  • Multidisciplinary Approach: Combination of  Maths, Statistics, AI, and Programming.
  • Data Analysis: It focuses on analyzing Big datasets.
  • Purpose: Extract valuable insights for business decisions.
  • Components: Include maths, statistics, programming, AI, and machine learning. 
  • Actionable Insights: Uncovering insights for decision-making


Applications of Data Science


Data science is one that is rapidly developing and interesting, and it continues to be one of the most prominent and sought-after job pathways for qualified individuals. Zoople offers a range of courses in Data Science, Machine learning and AI, for which it is renowned to be the best Data Science Institute in Kochi.


What are the applications of data science?


  • Business Intelligence and Analytics: Analyzing data to make informed business decisions and uncover insights
  • Healthcare and Medicine: Using data to improve patient care, disease prediction, and treatment
  • Finance: Managing risks, detecting fraud, and optimizing financial operations 
  • Marketing and advertising: Targeting customers and optimizing marketing campaigns
  • E-commerce: Sales optimization is achieved, thereby excelling with an enhanced customer experience in the ecommerce sectors
  • Manufacturing and Supply Chain: Streamlining Production and Optimising Supply Chains
  • Energy and Utilities: Monitoring and Optimising Energy Consumption and Production.
  • Transportation and Logistics: Enhancing Transportation Efficiency and Reducing Costs . 
  • Government and Public Policy: Informing policy decisions and improving public services
  • Environmental Science: Studying climate change, wildlife conservation, natural resource management, and many more.


What do we study in data science?


A true sign of brilliance in the field of data science is the Artificial Intelligence course. We see that data science is an area or field of study that involves various subjects, mainly mathematics, statistics, machine learning, AI, and deep learning.


Mathematics and Statistics:


Mathematics is a fundamental part of data science, which helps to better understand the machine learning algorithms for manipulating and transforming huge data sets into insights and building custom ML models.


Statistics is one of the main pillars of data science. Data analysis in data science requires both mathematics and statistics.


We cover mathematics essential for data science with a compact syllabus but with better understanding by covering all topics in an interactive Python way.


Machine Learning 


Machine learning is a subset of data science that focuses on the development of algorithms and models that enable computers to learn and make predictions or decisions from data.


The key element in the rapidly expanding discipline of data science is machine learning. To produce classifications or predictions, algorithms are taught using statistical techniques and it is highly applicable in finding insights in data mining projects.


Data science puts forward a powerful tool, machine learning, which allows data scientists to automate the extraction of insights from data, make predictions, and optimize processes in a wide range of applications across industries. It’s the technology behind many of the intelligent systems we interact with every day.


Deep Learning 


Machine learning is termed the superset of deep learning as they both cover a significant base from each other. By using deep learning techniques, you get the data processed in a way similar to the human brain and gain a greater range of control over the data. Deep learning models produce accurate insights and predictions and can recognize complex patterns in various data models like pictures, sounds, and text.



Zoople Technologies offers the most advanced artificial intelligence course in Kochi. 

Artificial intelligence can be defined as a superset of machine learning, deep learning, computer vision, natural language processing, etc. Generally, when an algorithm mimics a little human-like behavior, we can say that it is AI. Computer vision algorithms can recognize human faces or classify images, generating text like Pre-trained Transformers are the best examples of AI.


Additionally, BIG DATA!


As we said, data science is a branch of computer science that deals with huge amounts of data. The real question is how to manage big, huge data.


The answer is big data (Hadoop).


This course will teach you how to manage a huge amount of data with operations such as ETL (Extract, Load, and Transform) and analysis of data, and build ML models on them accordingly.


Who can study data science?


1. Undergraduates 

2. Professionals in IT

3. Statisticians and mathematicians

4. Domain Experts 

5. Career Changers 

6. Data Analysts 

7. Researchers 

8. Entrepreneurs 

9. Anyone interested in data


Boost your career with this data science course in Kochi. The course covers everything from very fundamental topics to advanced and trending topics in data science in machine learning.


After finishing this course


Data science is called one of the highest-paying jobs of the 21st century. After finishing our courses on data science and machine learning,  you’ll be hired for job roles like Data Scientist, Data Analyst, Data Engineer, and ML Practitioner.


As we know, everyone is unique in their skills; some of us are good at coding, some of us are good mathematicians, and so on. Lets see some of the highest-paying jobs after finishing the course;


1)Data Analysts 

If you are good at analyzing data and visualizing it and are interested in that field, you can opt for this career in the data science world. Analysis of data and visualization are the fundamental steps in data science, so you play an important role.


2) Data Engineers

Data engineers are responsible for the collection and maintenance of data. Finding the hidden patterns in the data Collaborate closely with the other teams and work with them according to work requirements.


3) Data Scientist

Data scientists are the quenchers for the hidden patterns in data, and they remain responsible for analyzing and cleaning as well. You must be very good at storytelling to explain to your customers the patterns you find. Most of the tasks depend on prediction, recommendation systems, fraud detection, and analyzing market risk. Using skills in Python, statistics, and the core of mathematics.


4) Machine Learning Practitioner

The core of a data science career is Machine learning. The next job level of a data scientist is ML practitioner. where you’ll create AI-related applications according to business requirements by using advanced ML models or using neural networks to satisfy the requirement. Sometimes you’re building your own models or customizing existing algorithms based on your data. Zoople Technologies is the right choice, if  you are looking into machine learning for beginners.


5) Data Architect

As we know, a data science career deals with huge amounts of data. Someone must be there to maintain all this data. To extract a portion of data from a large amount of data and process it according to business requirements. And again, transform the results and store them.


These are the primary jobs of a data architect with knowledge of big data and Apache Spark, which are also included in our course. Almost every industry today requires AI power, so your hiring chances will be very high.


Why should you choose our course?


1. This course is designed in such a way that you can start with no code experience and become an expert in coding and ML domain knowledge.

2. Learn the mathematics behind ML in detail using Python. There, you become an expert in both Python and mathematics.

3. The entire machine learning is divided into machine learning and deep learning as two modules to have in-depth knowledge of ML.

4. A Big Data course is added to this course to help you choose more career options. 

5. After each module of the course, you will have projects to learn.

6. We believe in learning by doing.


Training Syllabus

  • Foundation of Probablity Theory
  • Probability Measures,Probability Laws and Rules
  • Conditional Probability and Bayes’ Theorem
  • Probability Distributions

  • Probability Versus Statistics
  • Populations, Samples, and Bias
  • Descriptive and Inferential Statistics
  • Outliers & Percentiles,Quartiles
  • Types of Statistical Distribution

  • Matrices Introduction and Matrix Operations
  • Vectors, Vector Spaces,Linear Independence
  • Analytical Geometry
  • Matrix Decomposition
  • Eigen values and EigenVectors
  • Eigendecomposition and Diagonalization
  • Singular Value Decomposition
  • LU decomposition
  • Introduction to Calculus
  • Graphs of Functions
  • Limits and Derivates
  • Vector Calculus
  • Multivariate Calculus
  • Gradient Vector Maxima & Minima Concepts

  • Python Introduction
  • Datatypes, Variables, Keywords and Identifiers
  • Python IDE's
  • Flow Controls in python
  • Python Functions
  • Types of Functions
  • Introduction to OOP Programming
  • Classes, Objects and Inheritence
  • Operator Overloading Data Abstraction
  • Data Structures in Python
  • File Handling in Python
  • Erros and Exceptions
  • Regular Expressions
  • Iterators,Generators and Closures
  • Properties,Decorators in Python
  • Date and Time Functions
  • Map filter and Reduce Functions in Python
  • Virtual Environments and Dependency management

  • Introduction to ML,Numpy & Pandas
  • Data Science Lifecycle
  • Supervised Machine Learning
  • Unsupervised Machine Learning
  • Data Analysis and Cleaning
  • Data Visualiztion using Matplotlib, Seaborn and Plotly
  • Regression Techniques: Prediction Models
  • Classification Algoritms and other Algothims
  • Dimensionality Reduction using PCA
  • Time Series Analysis

  • Introduction to Neural Networks,
  • Understanding Activation Function, Learning Rate, Cost Function
  • Your First Neural Network
  • Gradient Descent Algorithms
  • Regressions using Neural Networks
  • Understanting Keras and tensorflow
  • Introduction to CNN,RNN
  • Number Dectection using Deep learning
  • Generative AI - A gentle introduction
  • Mini Project

  • Understanding Computer Vision
  • Introduction to OpenCV
  • Face Detection using OpenCV
  • Emotion Detection using OpenCV and VGG16-CNN
  • Introduction to YOLO

  • Text Preprocessing
  • Noise Removal
  • Feature Engineering on Text Data
  • Word Embeddings
  • Project : Sentiment Analysis over Twitter

  • Understanding Data and Big Data
  • Characterstics of Big Data
  • Where Hadoop fits in in Big Data
  • Hadoop Installation
  • Architecture of Hadoop
  • Hadoop Ecosystem (hdfs and mapReduce)
  • HDFS Commands
  • Pig Architecture
  • Pig Data Processing Operators
  • Hive and HiveQL
  • Apache Sqoop and Flume

  • Introduction to Spark
  • Spark vs MapReduce
  • Spark Components and Architecutre
  • Programming with RDDS
  • Parallel Processing in Spark
  • Spark Streaming
  • Apache Kafka
  • PySpark Projects