Transform Your Career and  become an Data Science Expert with our Comprehensive Course


Explore the Wonders of Data Science in Our Comprehensive Training!

Introduction to the course

Data science is a multidisciplinary field that involves extracting insights and knowledge from data through various methods such as data analysis, machine learning, and statistical modelling. It encompasses the processes of collecting, cleaning, analyzing, and interpreting data to uncover patterns, make predictions, and support decision-making in diverse domains.

Benefits of studying Data science

Studying data science offers several benefits, including enhanced job prospects and career opportunities in a rapidly growing field, the ability to analyze and interpret complex data to derive actionable insights, proficiency in utilizing machine learning and statistical techniques for predictive modelling and pattern recognition, the opportunity to work with diverse industries and domains, and the potential to contribute to innovation and decision-making by leveraging the power of data.

Who can take up the Data Science  course?

The data science course suits a wide range of individuals who have an interest in working with data and deriving insights from it. Some of the target audiences for data science courses include:

  1. Graduates and Students: Recent graduates or current students who want to gain expertise in data science to enhance their employability and excel in the data-driven job market.

IT Professionals: Individuals with a technical background who want to transition into data science and learn how to apply their skills to analyze and interpret data.

Job Outcomes

Completing a data science course can lead to various job outcomes and career opportunities in the field of data science and related domains like , Data Analyst, Machine Learning Engineer, Business Analyst, Data Engineer, AI Engineer, Data Product Manager and many more.

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  • Hands on Training with case studies , assignements
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  •  Recordings of every sessions even if its offline .
  • Industry based project scenarios
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Carnival Infopark Phase I, Infopark Rd, Infopark Campus, Kochi, Kerala 682042

Data Science

Unravel Insights, Drive Decisions. Empowering Success through Data-driven Discoveries

This Course Includes


Duration 2.5 months
0.5 Skill training


Certified course
Internship based on PE
Grooming sessions
Performance evaluations
Recorded sessions
Extra learning sessions
Weekly assignments
Main Project
Free Access to LMS
weekly assignments
Industry relevant assessments
Experienced Trainers from MNCs
Mock interviews and Interview specific support
third party assessment : Pre -Mid -post
Update on Job vacancy in and around Infopark


Duration 2.5 months
0.5 months training
3 months internship



Certified course
Confirmed Paid Internship
Sessions by Industry Experts
Flexible pricing options
24×7 learner assistance and support
Placement based on PE
Grooming sessions
Performance evaluations
Recorded sessions
Extra learning sessions
Weekly assignments
Main Project
Free Access to LMS
weekly assignments
Industry relevant assessments
Experienced Trainers from MNCs
Mock interviews and Interview specific support
third party assessment : Pre -Mid -post
Update on Job vacancy in and around Infopark


Duration 2.5 months
0.5 months training
3 months internship with stipend
job placement


Certified course
Confirmed Paid Internship
Sessions by Industry Experts
Flexible pricing options
24×7 learner assistance and support
Placement based on PE
Grooming sessions
Performance evaluations
Recorded sessions
Extra learning sessions
Weekly assignments
Main Project
Free Access to LMS
weekly assignments
Industry relevant assessments
Experienced Trainers from MNCs
Mock interviews and Interview specific support
third party assessment : Pre -Mid -post
Update on Job vacancy in and around Infopark


1) Data Analysis With MS-Excel

1.1 Excel Fundamentals
– Reading the Data, Referencing in formulas, Name Range, Logical Functions, Conditional Formatting, Advanced Validation, Dynamic Tables in Excel, Sorting and Filtering
– Working with Charts in Excel, Pivot Table, Dashboards, Data And File Security
– VBA Macros, Ranges and Worksheet in VBA
– IF conditions, loops, Debugging, etc.

1.2 Excel For Data Analytics
– Handling Text Data, Splitting, combining, data imputation on text data, Working with Dates in Excel, Data Conversion, Handling Missing Values, Data Cleaning, Working      with Tables in Excel, etc.

1.3 Data Visualization with Excel
– Charts, Pie charts, Scatter and bubble charts
– Bar charts, Column charts, Line charts, Maps
– Multiples: A set of charts with the same axes, Matrices, Cards, Tiles

1.4 Excel Power Tools
– Power Pivot, Power Query and Power View

1.5 Classification Problems using Excel
– Binary Classification Problems, Confusion Matrix, AUC and ROC curve
– Multiple Classification Problems

1.6 Information Measure in Excel
– Probability, Entropy, Dependence – Mutual Information

1.7 Regression Problems Using Excel
– Standardization, Normalization, Probability Distributions
– Inferential Statistics, Hypothesis Testing, ANOVA, Covariance, Correlation
– Linear Regression, Logistic Regression, Error in regression, Information Gain using Regression

2) Building Blocks for Python and ML (Pre-Learning)

2.1 Programming Basics
– Introduction to programming -Computer programs and business use – Database and its requirement in the softwareapplications.
– What id an IDE – Integrated development environment.
– Different programming languages, High level vs Low level languages
– Language translators – Complier and Interpreter, Why syntax rules?
– Programming basics: variables,INC rules: Identifier Naming Conventions, Datatypes,Operators.
– Control flow statements: Conditional statements and Loops.
– Functions and UDFS.
– Logic building and Pseudo codes.

2.2 Introduction to Basic Statistics
– Introduction to Statistics
– Measures of central tendencies
– Measures of variance
– Measures of frequency
– Measures of Rank
– Basics of Probability, distributions
– Conditional Probability (Bayes Theorem)

2.3 Introduction to Mathematical foundations
– Sets & Functions
– Introduction to Linear Algebra
– Matrices Operations
– Introduction to Calculus
– Derivatives & Integration – Maxima, minima
– Area under the curve

3) Python For Data Science (1/2)

3.1 Python Essentials (Core) -Overview of Python-Starting with Python
– Why Python for data science? – Anaconda vs. python
– Introduction to installation of Python -Introduction to Python IDE’s (Jupyter/python)
– Concept of Packages – Important packages
– NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc
– Installing & loading Packages & Name Spaces
– Data Types & Data objects/ structures (strings, Tuples, Lists, Dictionaries)
– List and Dictionary Comprehensions
– Variable & Value Labels – Date & Time Values
– Basic Operations – Mathematical/ string/date
– Control flow & conditional statements
– Debugging & Code profiling
– Python Built-in Functions (Text, numeric, date, utility functions)
– User defined functions – Lambda functions
– Concept of apply functions
– Python – Objects – OOPs concepts
– How to create & call class and modules?

3.2 Operations with NumPy (Numerical Python) -What is NumPy?
– Overview of functions & methods in NumPy
– Data structures in NumPy
– Creating arrays and initializing – Reading arrays from files
– Special initializing functions -Slicing and indexing
– Reshaping arrays
– Combining arrays -NumPy Maths

3.3 Overview of Pandas
– What is pandas, its functions & methods -Pandas Data Structures (Series &Data Frames)
– Creating Data Structures (Data import- reading into pandas)

3.4 Cleansing Data with Python
– Understand the data
– Sub Setting/Filtering / Slicing Data
– Using brackets
– Using indexing or referring with column names/rows
– Using functions
– Dropping rows & columns
– Mutation of table (Adding/deleting columns)
– Binning data (Binning numerical variables in tocategorical variables)
– Renaming columns or rows -Sorting (by data/values, index)
– By one column or multiple columns
– Ascending or Descending
– Type conversions
– Setting index
– Handling duplicates /missing/Outliers -Creating dummies from categorical data (using get dummies()) -Applying functions to all the variables in a data frame (broadcasting)
– Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc.)

3.5 Data Analysis using Python
– Exploratory data analysis
– Descriptive statistics, Frequency Tables and summarization
– Uni-variate Analysis (Distribution of data & Graphical Analysis)
– Bi-Variate Analysis/Cross Tabs, Distributions & Relationships, Graphical Analysis)

4) Python For Data Science (2/2)

4.1 Data Visualization with Python
– Introduction to Data Visualization
– Introduction to Matplotlib
– Basic Plotting with Matplotlib
– Line Plots

4.2 Basic Visualization Tools
– Area Plots
– Histograms/Density plots
– Bar Charts/Stacked charts
– Pie Charts
– Box Plots -Scatter Plots
– Sub Plots

4.3 Statistical Methods & Hypothesis Testing
– Descriptive vs. Inferential Statistics -What is probability distribution? -Important distributions (discrete & continuous distributions)
– Deep dive of normal distributions and properties
– Concept of sampling & types of sampling
– Concept of standard error and central limit theorem
– Hypothesis Testing & Applications
– Statistical Methods – Z/t-tests (One sample, independent, paired), ANOVA, Correlation and Chi-square

5) Predictive Modeling & Machine Learning

5.1 Introduction to Predictive Modeling
– Concept of model in analytics and how it is used?
– Common terminology used in modeling process
– Types of Business problems – Mapping of Algorithms
– Different Phases of Predictive Modeling -Data Exploration for modeling
– Exploring the data and identifying any problems with the data (Data Audit Report)
– Identify missing/Outliers in the datal
–  Visualize the data trends and patterns

5.2 Introduction to Machine Learning
– Applications of Machine Learning -Supervised vs Unsupervised Learning vs. Reinforcement Learning
– Overall process of executing the ML project
– Stages of ML Project
– Concept of Over fitting and Under fitting (Bias-Variance Trade off) & Performance Metrics
– Concept of feature engineering -Regularization (LASSO, Elastic net and Ridge)
– Types of Cross validation(Train & Test, K-Fold validation etc.)
– Concept of optimization
– Gradient descent algorithm Cost & optimization functions
– Python libraries suitable for Machine Learning

5.3 Supervised Learning:
Regression problems
– Linear Regression
– Non-linear Regression
– K-Nearest Neighbor
– Decision Trees
– Ensemble Learning – Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
– Support Vector Regressor

5.4 Supervised Learning:
Classification problems
– Logistic Regression
– K-Nearest Neighbor
– Naive Bayes Classifier
– Decision Trees
– Ensemble Learning – Bagging, Random Forest, Adaboost, Gradient Boost, XGBoost
– Support Vector Classifier

5.5 Unsupervised Learning
– Principle Component Analysis
– K-Means Clustering
– Density-Based Clustering

5.6 Recommender Systems
– Market Basket Analysis (MBA) -Content-based recommender systems
– Collaborative Filtering

5.7 Time Series Forecasting
– What is forecasting?
– Applications of forecasting -Time Series Components and Decomposition
– Types of Seasonality
– Important terminology: lag, lead,Stationary, stationary tests, auto correlation & white noise, ACF & PACF plots, auto regression, differencing
– Classification of Time Series Techniques (Uni-variate & Multivariate)
– Time Series Modeling & Forecasting Techniques
– Averages (Moving average, Weighted Moving Average)
– ETS models (Holt Winter Methods)
– Seasonal Decomposition
– Regression
– Evaluation of Forecasting Models

6) Text Mining using NLP

6.1 Introduction to Text Mining
– Text Mining – characteristics, trends – Text Processing using Base Python & Pandas, Regular Expressions
– Text processing using string functions & methods
– Understanding regular expressions -Identifying patterns in the text using regular expressions

6.2 Text Processing with modules like NLTK, sklearn
– Getting Started with NLTK – Introduction to NLP & NLTK
– Introduction to NLTK Modules (corpus, tokenize, Stem, collocations, tag, classify, cluster, tbl, chunk, Parse, ccg, sem, inference, metrics, app, chat, toolbox etc)

6.3 Initial data processing and simple statistical tools
– Reading data from file folder/from text file, from the Internet & Web scrapping, Data Parsing
– Cleaning and normalization of data -Sentence Tokenize and Word Tokenize, Removinginsignificant words (“stop words”), Removing specialsymbols, removing bullet            points and digits, changing letters to lowercase, stemming/lemmatization/chunking
– Creating Term-Document matrix
– Tagging text with parts of speech
– Word Sense Disambiguation
– Finding associations
– Measurement of similarity between documents and terms
– Visualization of term significance in the form of word clouds

6.4 Advanced data processing and visualization
– Vectorization (Count, TF-IDF, Word Embedding’s)
– Sentiment analysis (vocabulary approach, based on Bayesian probability methods)
– Name entity recognition (NER) -Methods of data visualization
– word length counts plot
– word frequency plots
– word clouds
– correlation plots
– letter frequency plot
– Heat map
– Grouping texts using different methods
– Language Models and n-grams —
Statistical Models of Unseen Data (Smoothing)

6.5 Text Mining – Predictive Modeling
– Semantic similarity between texts
– Text Segmentation
– Topic Mining (LDA)
– Text Classification(spam detection, sentiment analysis, Intent Analysis)

7) Introduction to AI & DL & Cloud Computing

7.1 Introduction to Artificial Intelligence (AI)
– Modern era of Al
– Role of Machine learning & Deep Learning in Al
– Hardware for Al (CPU vs. GPU vs. FPGA)
– Software Frameworks for Al & Deep Learning
– Key Industry applications of Al

7.2 Introduction to Deep Learning
– What are the Limitations of Machine Learning?
– What is Deep Learning?
– Advantage of Deep Learning over Machine learning
– Reasons to go for Deep Learning -Real-Life use cases of Deep Learning -Overview of important python packages for Deep Learning

7.3 Artificial Neural Network -Overview of Neural Networks
– Activation Functions, hidden layers, hidden units
– Illustrate & Training a Perceptron -Important Parameters of Perceptron – Understand limitations of A Single Layer Perceptron
– Illustrate Multi-Layer Perceptron
– Understand Backpropagation – Using Examples
– Implementation of ANN in Python-Keras

7.4 Introduction to Google Colab/Kaggle workbooks

7.5 Introduction to Cloud Computing – What is Cloud Computing? Why it matters?
– Traditional IT Infrastructure vs. Cloud Infrastructure
– Cloud Companies (Microsoft Azure, GCP, AWS) & their Cloud
– Services (Compute, storage, networking, apps, cognitive etc.)
– Use Cases of Cloud computing -Over view of Cloud Segments: laaS, PaaS, SaaS
– Overview of Cloud Deployment Models
– Overview of Cloud Security
– AWS vs. Azure vs. GCP

8) Introduction to ML-Ops & Model Deployment (self-paced)

8.1 Introduction to ML-Ops & Model Deployment (self-paced)
– What is MLOps
– MLOps vs. DevOps vs. Data Engineering
– Why MLOps is important?
– ML Engineering Pipeline
– How to implement MLOps?
– Understand end to end MLOps solution

8.2 Deployment of ML Model in the cloud
– What is model deployment?
– Ways of deployment of models
– Introduction to Flask
– How to create simple app?
– Deployment of ML model in the cloud



Q: What will I learn in a Data Science course?

A: In a Data Science course, you will learn a range of topics and techniques related to data analysis and modeling. This may include data preprocessing, exploratory data analysis, statistical analysis, machine learning algorithms, predictive modeling, data visualization, and big data analytics. The course may also cover programming languages like Python or R, as well as tools and libraries commonly used in Data Science.

Q: Are there any prerequisites for enrolling in a Data Science course?

A: Prerequisites for Data Science courses can vary depending on the course level and content. Some introductory courses may not have specific prerequisites and assume minimal programming and mathematical knowledge. However, more advanced courses may require a solid understanding of programming concepts, mathematics (such as statistics, linear algebra, and calculus), and some familiarity with data analysis tools.

Q: What is the duration of a Data Science course?

A: The duration of a Data Science course can vary depending on the course’s depth, content, and learning format. Some introductory courses may be completed in a few weeks, while comprehensive courses covering advanced topics and practical projects may extend over several months. It’s important to review the course details for estimated time commitments.

Q: Can I get hands-on experience in a Data Science course?

A: Reputable Data Science courses often provide hands-on exercises, projects, and real-world datasets to provide practical experience. These activities allow you to apply Data Science techniques, work with data, implement machine learning algorithms, perform data analysis, and gain practical experience in solving Data Science problems.

Q: Why should I learn Data Science?

A: Learning Data Science can be beneficial for several reasons. It is a rapidly growing field with high demand for skilled professionals. Data Science skills can open up numerous career opportunities across various industries, including technology, finance, healthcare, e-commerce, and more. Data Science enables you to uncover valuable insights from data, contribute to decision-making processes, and drive innovation.

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