Data Science Training And Internship Kollam

How to Make Data-Driven Decisions with the Help of a Data Science Course

Businesses in the modern digital era mainly depend on data to make wise decisions. The need for qualified Data Scientists who can draw conclusions and information from large, complicated data sets has consequently increased. You may acquire the fundamental knowledge and abilities required to be successful in this fascinating and rapidly expanding field by enrolling in Futura Labs Data Science Course in Kollam.

How Come Data Science Is Important to Learn?

Experts in data interpretation and analysis who can help businesses make informed decisions are in high demand in the quickly expanding field of data science. Having an understanding of data science can provide you with a competitive advantage and improve your prospects of obtaining a lucrative position in a range of sectors, such as e-commerce, healthcare, and finance.

Prospects for a Career in Data Science

With great need for professionals in fields like artificial intelligence, machine learning, data engineering, and data analysis, data science provides enormous and intriguing employment potential. According to a LinkedIn analysis, employment in data science is expected to rise at a rate of 37% by 2027, placing them among the top 15 developing jobs globally.

Why Should You Take Your Data Science Course at Futura Labs ?

At Futura Labs, we are committed to giving our students the best education possible so they can pursue successful careers. With online lessons you can do at your own pace, our Data Science course is made to be accessible and adaptable. Additionally, we offer tailored support and direction to make sure you have the tools necessary for success.

Enroll in Kollam’s Futura Labs Data Science Course Now!

Enroll in Futura Labs’ data science course in Kollam right now if you’re prepared to take advantage of the field’s job opportunities and acquire the necessary skills. You will acquire the abilities and information needed to be successful in this fascinating and rapidly expanding industry thanks to our extensive curriculum and knowledgeable professors.

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Calicut

Call: +91 799 44 200 46

Call: +91 799 44 200 45

Kochi

Call: +91 977 80 430 02

Call: +91 994 63 268 88

DATA SCIENCE INTERNSHIP SYLLABUS : 4 Months

MODULE 1 : INTRODUCTION

Algorithm
Flow Chart
GIT

MODULE 2 : STATISTICS

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

MODULE 3 : PYTHON LIBRARIES

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

MODULE 4 : EDA

Data Wrangling and Manipulation
Descriptive statistics
Identifying Patterns and Outliers
Missing value and Outliers
Imputation techniques
Transformation techniques
Standardization
Normalization

MODULE 5 : MACHINE LEARNING

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

MODULE 6 : DEEP LEARNING

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

MODULE 7 : NATURAL LANGUAGE PROCESSING

Introduction to NLP
Spacy
Pipeline-transformers
Text preprocessing
Tokenization
Stemming
Lemmatization

MODULE 8 : PROJECT

Mini Project - Using machine learning
Main Project - Both machine learning and deep learning

DATA SCIENCE INTERNSHIP SYLLABUS : 6 Months

MODULE 1 : INTRODUCTION

Algorithm
Flow Chart
GIT

MODULE 2- STATISTICS

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

MODULE 3 : PYTHON BASICS

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

MODULE 4 : PYTHON LIBRARIES

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

MODULE 5 : EDA

Data Wrangling and Manipulation
Descriptive statistics
Identifying Patterns and Outliers
Missing value and Outliers
Imputation techniques
Transformation techniques
Standardization
Normalization

MODULE 6 : MACHINE LEARNING

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

MODULE 7 : DEEP LEARNING

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

MODULE 8 : NATURAL LANGUAGE PROCESSING

Introduction to NLP
Spacy
pipeline-transformers
Sentiment analysis
Text preprocessing
Tokenization
Stemming
Lemmatization
Word embedding techniques

MODULE 9 : PROJECT

Mini Project - Using machine learning
Main Project - Both machine learning and deep learning

Duration

4 Months, 3 Days a Week, 3 Hours/day

Course Fees

Fees (Offline Mode) : 30000

Fees (Online Mode) : 27000

Duration

6 Months, 3 Days a Week, 3 Hours/day

Course Fees

Fees (Offline Mode) : 50000

Fees (Online Mode) : 45000

DATA SCIENCE INTERNSHIP SYLLABUS : 4 Months

DATA SCIENCE INTERNSHIP SYLLABUS : 6 Months

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