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 Alappuzha.

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 Alappuzha’s Futura Labs Data Science Course Now!

Enroll in Futura Labs’ data science course in Alappuzha 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.

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
  • 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

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
DurationDuration
4 Months, 3 Days a Week, 3 Hours/day
FeeCourse Fees
DurationDuration
6 Months, 3 Days a Week, 3 Hours/day
FeeCourse Fees

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