Data Science Training And Internship Kochi

Data Science Course: Empowering You to Make Data-Driven Decisions

In today’s digital age, businesses rely heavily on data to make informed decisions. As a result, the demand for skilled Data Scientists who can extract insights and knowledge from complex data sets has skyrocketed. With Futura Labs Data Science Course in Kochi, you can learn the essential skills and techniques needed to succeed in this exciting and growing field.

Why Learn Data Science?

Data Science is a rapidly growing domain with huge demand for professionals who can interpret and analyze data to inform business decisions. Learning Data Science can fetch you a competitive edge and increase your chances of landing a high-paying job in a variety of industries, including finance, healthcare, and e-commerce.

Career Prospects in Data Science

Data Science offers immense, exciting career prospects, with high demand for professionals in areas such as data analysis, data engineering, machine learning, and artificial intelligence. A report by LinkedIn, reveals that Data Science jobs are among the top 15 emerging jobs in the world, with a projected growth rate of 37% by 2027.

Concepts that trainees can learn at Futura Labs, Kochi

  • Data Science with Python
  • Machine Learning
  • Data Science with R Programming
  • Data Science Capstone
  • Big Data Hadoop and Spark Developer

Our able and experienced instructors will guide you through each module, providing hands-on experience with real-world data sets and tools. By the end of the course, you’ll acquire the skills and confidence needed to identify and analyze data like a pro. This can open windows to incredible jobs.

Why Choose Futura Labs Kochi for Your Data Science Course?

At Futura Labs, we believe in providing superlative education that empowers our students to succeed in their careers. Our Data Science Course is designed to be flexible and accessible, with online modules that can be completed at your own pace. We also provide personalized support and guidance, ensuring that you have the resources you need to succeed.

Enroll in Futura Labs Data Science Course in Kochi Today

If you’re ready to take advantage of the career prospects in Data Science and develop the skills needed to succeed, enroll in Futura Labs Data Science Course in Kochi today. With our comprehensive curriculum and experienced instructors, you’ll gain the skills and knowledge needed to succeed in this exciting and growing field.

DATA SCIENCE INTERNSHIP SYLLABUS : 4 Months

  • Algorithm
  • Flow Chart
  • GIT
  • Introduction to Statistical Analysis
  • Descriptive statistics
  • Inferential statistics
  • Mean, Median, Mode
  • Standard deviation, Variance, Range
  • Outliers
  • Quartile range
  • Interquartile range
  • Probability
  • Estimation and Hypothesis
  • Testing
  • Scatter Diagram
  • Missing values
  • Imputation Techniques
  • Covariance
  • Correlation and Regression
  • Random variable
  • Normal distribution
  • Skewness
  • Kurtosis
  • Central limit theorem
  • Anova

3.1 : NumPy

  • Introduction to numpy
  • Numpy arrays
  • Operations on arrays
  • Indexing & slicing
  • Numpy functions

3.2 : Pandas

  • Introduction to pandas
  • Data manipulation
  • Series
  • Data frames
  • Importing and exporting files
  • Basic functions
  • Date range
  • Group by
  • Merging
  • Concatenation
  • Pivot table
  • Contingency table

3.3 : Matplotlib

  • Introduction to Matplotlib
  • Different Types of Charts
  • Bar chart
  • Line Chart
  • Scatter Chart
  • Pie Chart
  • Stack plot
  • Data Wrangling and Manipulation
  • Descriptive statistics
  • Identifying Patterns and Outliers
  • Missing value and Outliers
  • Imputation techniques
  • Transformation techniques
  • Standardization
  • Normalization
     
  • Introduction to Artificial Intelligence and Machine learning
  • Regression and classification

5.1 : Supervised Learning

  • Linear Regression
  • Logistic Regression
  • Naive Bayes
  • Decision Trees
  • Random Forest
  • Support Vector Machines
  • K-Nearest Neighbor
  • Model validation
  • Model Evaluation
  • Gridsearchcv

5.2 : Unsupervised learning

  • Clustering
  • Hierarchical clustering
  • K-Means clustering

5.3 : Ensemble Learning

  • Adaboost
  • Gradient Boosting
  • Introduction to deep learning
  • Introduction to tensorflow, Keras
  • Introduction to computer vision

6.1 : Artificial Neural Network

  • Artificial Neural Network
  • Perceptron
  • Activation Functions and Types
  • Weight Initialization Technique
  • Optimization
  • Adagrad
  • Adam
  • Regularization
  • Dropout

6.2 : Convolutional Neural Network

  • Convolutional Neural Network
  • CNN Architecture
  • Convolution, pooling, flattening
  • Image classification with CNN
  • Transfer Learning(Alexnet, vgg16,)
  • Opencv Basics

6.3 : Recurrent Neural Network

  • Recurrent Neural Network
  • Architecture of RNN
  • LSTM
  • Bi-directional LSTM
  • Introduction to NLP
  • Spacy
  • Pipeline-transformers
  • Text preprocessing
  • Tokenization
  • Stemming
  • Lemmatization
  • Mini Project - Using machine learning
  • Main Project - Both machine learning and deep learning

DATA SCIENCE INTERNSHIP SYLLABUS : 6 Months

  • Algorithm
  • Flow Chart
  • GIT

 

  • Introduction to Statistical Analysis
  • Descriptive statistics
  • Inferential statistics
  • Mean, Median, Mode
  • Standard deviation, Variance, Range
  • Outliers
  • Quartile range
  • Interquartile range
  • Probability
  • Estimation and Hypothesis
  • Testing
  • Scatter Diagram
  • Missing values
  • Imputation Techniques
  • Covariance
  • Correlation and Regression
  • Random variable
  • Normal distribution
  • Skewness
  • Kurtosis
  • Central limit theorem
  • Anova
  • Python introduction
  • Variables
  • Data types
  • String functions
  • Data types
  • Conditional statements
  • Loop
  • Functions
  • Oops
  • Inheritance and types
  • Exception Handling
  • File Handling
  • Module Handling
  • Python Regex

4.1 : NumPy

  • Introduction to numpy
  • Numpy arrays
  • Operations on arrays
  • Indexing & slicing
  • Numpy functions

4.2 : Pandas

  • Introduction to pandas
  • Data manipulation
  • Series
  • Data frames
  • Importing and exporting files
  • Basic functions
  • Date range
  • Group by
  • Merging
  • Concatenation
  • Pivot table
  • Contingency table

4.3 : Matplotlib

  • Introduction to Matplotlib
  • Different Types of Charts
  • Bar chart
  • Line Chart
  • Scatter Chart
  • Pie Chart
  • Stack plot
  • Data Wrangling and Manipulation
  • Descriptive statistics
  • Identifying Patterns and Outliers
  • Missing value and Outliers
  • Imputation techniques
  • Transformation techniques
  • Standardization
  • Normalization
  • Introduction to Artificial Intelligence and Machine learning
  • Regression and classification

6.1 : Supervised Learning

  • Linear Regression
  • Logistic Regression
  • Naive Bayes
  • Decision Trees
  • Random Forest
  • Support Vector Machines
  • K-Nearest Neighbor
  • Model validation
  • Model Evaluation
  • Gridsearchcv

6.2 : Unsupervised learning

  • Clustering
  • Hierarchical clustering
  • K-Means clustering

6.3 : Ensemble Learning

  • Bagging
  • Adaboost
  • Gradient Boosting
  • Introduction to deep learning
  • Introduction to tensorflow, Keras
  • Introduction to computer vision

7.1 : Artificial Neural Network

  • Artificial Neural Network
  • Perceptron
  • Activation Functions and Types
  • Weight Initialization Technique
  • Optimization
  • Adagrad
  • Adam
  • Regularization
  • Dropout

7.2 : Convolutional Neural Network

  • Convolutional Neural Network
  • CNN Architecture
  • Convolution, pooling, flattening
  • Image classification with CNN
  • Transfer Learning(Alexnet, vgg16,)
  • Opencv Basics
  • Edge detection
  • Object detection
  • Face Recognition
  • Autoencoders

7.3 : Recurrent Neural Network

  • Recurrent Neural Network
  • Architecture of RNN
  • LSTM
  • LSTM Networks
  • Bi-directional LSTM
  • Introduction to NLP
  • Spacy
  • pipeline-transformers
  • Sentiment analysis
  • Text preprocessing
  • Tokenization
  • Stemming
  • Lemmatization
  • Word embedding techniques
  • Mini Project - Using machine learning
  • Main Project - Both machine learning and deep learning
batchNext Batch
21
April
(Offline & Online)
29
April
(Offline & Online)
DurationDuration
4 Months, 6 Days a Week, 3 Hours/day
FeeCourse Fees
DurationDuration
6 Months, 6 Days a Week, 3 Hours/day
FeeCourse Fees

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      Frequently Asked Questions

      Data Science is an interdisciplinary field that involves using data to make informed decisions. It combines elements of statistics, computer science, and domain expertise to extract insights and knowledge from complex data sets. Data Science is essential in today's business world because it allows companies to make data-driven decisions, leading to better outcomes and increased profitability.
      Futura Lab's Data Science course covers a wide range of topics, including Data Science with Python, Machine Learning, Data Science with R Programming, Data Science Capstone, and Big Data Hadoop and Spark Development. Trainees will gain a comprehensive understanding of the essential concepts and tools used in Data Science.
      Our Data Science course is designed for individuals who want to learn how to extract insights and knowledge from complex data sets. The course is ideal for professionals working in a variety of industries, including finance, healthcare, e-commerce, and media & entertainment. Students who want to pursue a career in Data Science can also undergo this course. At Futura Lab, we ensure that you excel in this course and become a knowledgeable Data Science professional.
      Frequently Asked Questions
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