## Key Highlights on Data Science Course

## Overview of Data Science Course in Chennai

The Data Science course certification shows the candidate’s ability to acquire complete subject knowledge and learn all the basic tools and algorithms used in Data Science.

### What is Data Science?

Data Science is a branch of study that brings together subject-matter expertise, programming abilities, and understanding of math and statistics to derive practical insights from data. To create artificial intelligence (AI) systems that can execute activities that often require human intelligence, data scientists use machine learning algorithms for data, text, pictures, video, audio, and more.

### Why become a Data Scientist?

According to Glassdoor and Forbes, demand for data scientists increase by 28% by 2026, indicating the profession’s stability and endurance; In the future, you can assist them in performing better by using data-based statistics. The demand for this quality of knowledge is already very high and only to grow. It's among the factors that make learning data science so crucial in the company culture.Therefore, if you desire a solid career, Data Science provides that opportunity.

### Why learn Data Science Course?

After Completing your Data Science Certification training, we guide you to apply for the top MNCs with the help of our placement support teams.

### Why do we learn Data Science at BTree Systems?

Right now, Data Science jobs are among the highest-paying ones in the market. The largest y-o-y growth (336.4%) was seen in the non-IT open positions in domestic enterprises. These companies posted 9,628 opportunities in 2022 compared to 2,206 in 2021. Over the same period, the proportion of open positions rose from 1.6% to 5.4% in percentage terms.

With 15 years of experience at the level of training and industry expert facilities, Btree System provides more than 60+ IT Training courses in more than ten branches in Chennai.

### What is the fundamental aspects of Data Science with Machine learning?

Data Science with machine learning is a multidisciplinary field that draws on the ideas of visualization, neural networks, chatbots, and cloud analytics to excavate data and insights for both structured and unstructured data. It aids to increasing the high level of security and privacy of secured data and directs data-driven decision making.

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## Career Transition on Data Science

60%

Avg Salary Hike

40 LPA

Highest Salary

500+

Career Transitions

300+

Hiring Partners

## Skills Covered for Data Science Certification

Deep Learning

Machine Learning

Statistics

Prediction algorithms

MLOps

Data Wrangling

Data visualization

NLP

Seaborn

## Tools Covered for Data Science Certification

## Data Science Course Fees

16

Sep

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23

Sep

SAT - SUN

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30

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## Data Science Corporate Training

Enroll in our corporate training program today and unlock the full potential of your Employees

## Curriculum for Data Science Certification Course in Chennai

### Introduction To Data Science

- Types of analytics
- What is machine learning
- What is Data Analysis
- Difference between Descriptive and predictive analytics.
- Analytics Project Life cycle

### Statistics

- introduction to Statistics
- Descriptive Statistics
- Inferential Statistics
- Central Limit Theorem
**Types of variables**- Nominal/Categorical
- Ordinal
- Interval/Ratio (Not skewed)
- Interval/Ratio (skewed)
**Central Tendency**- Mean
- Weighted mean
- Trimmed mean/Truncated Mean
- InterQuertile mean
- Trimmed Mean
- Median
- Mode
- Measure of Statical dispersions

### Different measures of Statistical Dispersions

- Varaince and Bessels correction
- Standard Deviation
- Standard Error
- IQR
- Range
- Mean absolute difference
- Median absolute deviation
- coefficient of variation
- Different measures of Statistical Dispersions
- Skewness & acceptable range
- Kurtosis & acceptable range
- Degrees of freedom
- Confidence Interval
**Probability**- Probability
- Venn diagram& probability tree
- counting (permutation & combination )
- Expectation
- Conditional probablilty
- Bayes theorem
- Maximum likehoodestimation
- Fishers iteration
- Montle carol simulation
- Probability Distributions
- Continuous Distributions
- Different types of Probability distributions
- (Normal,uniform, T, F, chi square, beta, gamma, exponentail)
**Descrete Distributions**- Different types of Probability distributions
- (bernoulli, binomial, possion, dirichlet)
- Conversion of distribution
- Correlation and auto correlation & correlation
- matrix
- Correlation ratio
- Sampling Techniques
- Different types o sampling techniques
- Sampling errors
- Sample size estimation
- Point estimation & margin of error
- creating sample using distribution & GMM
- Multi Colinearity
- Co-variance
- Type of test (Parametric and non parametric) &
- assumption

### Hypothesis Testings

- Z-test
- T-test
- ChiSquare test
- Power test, Beta test
- ANOVA (one way and two way)
- F-test & f score

### Linear Algebra

- Linear Algebra basics
- Need to Linear algebra in Machine Learning
- Matrix and scalar multiplication
- Matrix addition, subtraction, division, Etc.,
- Matrices and vectrors
- Matrix inverse and transpose
- Matrix rotation & span
- Matrix scaling

### Python

- Why Python for data analysis
- Python 2.7 vs python 3.6
- How to install Anaconda
- Running a few simple programs using python
**Python objects**- Lists
- Strings
- Sets
- File objects
- Tuples
- Dictionaries
- Arrays, Data frames in python
**Python Libraries**- Numpy
- Scipy
- Matplotlib
- Pandas
- Scikit Learn
- Seaborn
- Additional Libraries
- OS
- Regular expressions
- BeautifulSoup
- Introduction to Series and Dataframes
- Visualisation on dataset using python
- Plot library
- Distibution analysis in python
- Box plot in python
- Comments in python
- Functions in python
- Conversion functions
- Math functions
- User defined Functions
- Parameters and arguments of functions
- Range function in python
- Recursive function
- Examples of Resursive functions
- String Methods
- Len ()
- Lower ()
- Upper
- Str
- String concatination
- Conditionals In python
**Data Science Course Content**- If loop
- If else
- If elif else
- Loops in python
- For loop
- While loop
- Mastering pandas
- What is pandas
- Benefits of using pandas
- Creating matrixes using numpy
- Statistical operators using Numpy
- Broadcasting in Python
- Array shape manipulations
- Data structures in pandas
- Series
- Dataframe
- Panel
- Various DataframeOperations
- Selection
- Deletion etc.
- Grouping, Merging,and Reshaping of Data
- Groupby
- Aggregate
- Transform
- Filtering
- Merging and joining (concatand append )
- Drop
- Apply fucntions in pandas
- Accesing the objects in python by index

### Machine Learning

- Data Exploration (EDA)
- Variable Identification
- Univariate analysis
- Continuous variable
- Categorical variable
- Bivariate Analysis
- Continious-Continious
- Caterogical and Categorical
- Categorical and Continious
- Missing Value Treatment
- Outlier Detection and Treatment
- Feature Engineering
- Variable transformation
- Variable /Feature Creation
- Data preprocessing
- What is data set.
- What is training set
- What is test set and need for test set
- Missing values
- Expectation-Maximization technique for missing value
- using Gradient
- Using full information maximum likelihood
- Using mice & input packages
- Feature scaling

### Machine Learning Feature Transformation

- Bining
- One hot encoding
- Response rate
- Frequency response
- Probability values
- Feature engineering
- Outliers
- Supervised Learning
- Unsupervised Learning

### Difference between Classification and Regression

- Model Metrics
- ROC Curves
- Confusion matrix
- Accuracy
- Recall & Precision
- F1-Score
- AIC & BIC
- R squared and Adjusted R squared
- Supervised Classification Algorithms

### Simple Linear Regression

- Assumptions of Linearregrssion
- Simple Linear regression Intution
- Simple Linear regression loss function
- Simple Linear regression cost function.
- What is gradient descent Gradient Descent
- What is Learning rate
- Learning curves
- Applying Linear regression on dataset using Python
- Variation inflation factor
- Effect of multicollinearity
- Effect of outliers

### Multiple Linear Regression

- Multiple Linearregression Intution
- Multiple Linear regression loss function
- Regualarisation
- The problem of overfitting
- Applying Linear regression on dataset usingPython
- Interepreting the Linear model built
- Feature selection using Backward elimination
- Feature selection using Backward elimination

### Logistic regression classification

- Assumptions of Logistic regression
- Sigmoid function
- Loss function of logisticregression
- Loss function of logisticregression intuition
- Cost function of Logisticregression
- Odds ratio
- Applying Logisticregression on datastusing Python
- Evaluating the model built

### Decision Tree Classification

- Decision tree intuition
- Entropy & information gain
- Aassumption
- Decision tree using Information gain and gini index
- Applying Decision tree on a dataset using Python
- Evaluating the model built

### Random Forest classification

- Random Forest intuition
- Random Forest using Information gain and gini index
- Out of box error
- Variable importance
- Applying Random forest on a dataset using Python

### Support Vector Machines classification

- Why SVM is so poweful?
- Difference between Linear Regression and SVM
- Mapping data t higher dimensions
- What is kernal
- Different types ofkernal
- Kernal trickin SVM
- Margin in SVM
- Slack variables in SVM
- Hard vectors and soft vectors in SVM
- Disadvantages of SVM
- Parctical exampleon a dataset

### Naive Bayes Classification

- What is Naive
- What is conditional probability
- Bayes Theorem
- How Naive Bayes works
- Smoothing Technique in Naïve bayes

### Supervised Regression Algorithms

- Decision tree Regression
- Random Forest Regression
- GLM (Poisson regression, spline)
- Support Vector Machines Regression

### Hierarchical clustering

- Different types of HC
- How HC work
- What is dendo grams
- Finding the number of clusters using Dendo grams
- Applying K means on a dataset using Python

### Unsupervised Algorithms

- K-means Clustering
- How K means work
- Finding the number of clusters
- Applying K means on a dataset using Python

### Associate Rule Mining

- What is market basket analysis
- Support, Confidence and Lift parameters
- Detailed explanation of How Apriori works
- Applying Apriori on a dataset using Apriori
- FP growth, collaborative filtering
- ECLAT
- Apriori VS ECLAT

### Applying ECLAT on a Dataset using python

- Ensembling methods
- What is ensembling methods
- Why Ensembling methods
- Bagging Concept
- Boosting concept
- Ada boost Algorithm
- What is weak learners
- Adaboost intuition
- How adaboostworks
- Applying Adabooston dataset using Python

### GBM

- What is GBM
- How GBM works
- Applying GBM on a dataset using python

### Deep Learning

- Introduction to Deep learning
- What is neuron
- What is neuron
- How ANN works
- Gradient descent
- Stochastic & batch gradient descent
- Backward propagation
- Installing Theano, Tensor flow and Keras
- ANN
- Business case Explanation
- Training ANN with Stochastic Gradient
- descent
- Evaluating the model
- Activation Functions
- Different types of activation functions
- Convolutional Neural Networks (CNN)
- CNN architecture
- Convolution
- Maxpooling
- Falttening
- Full connection
- Softmax VS Cross Entropy

### Convolutional neural Network (CNN)

- Explaining the business case
- Explaining the different steps in CNN in
- python
- Hyperparemters tuning
- Evaluating the model built
- What is RNN
- Introduction to RNN
- LSTM Introduction
- LSTM in Python
- Drop outs & weight initialization, bias
- configuration

### Natural Language Processing

- What is NLP
- Applications of NLP in day to day life
- Why NLP
- Different type sof package available fot NLP
- Stanforld NLP package in python
- Preprocessing Steps in NLP
- Stop word removal
- Tokenization
- Stemming
- Parts of Speech tagging(POS)
- Named Entity Recognition(NER)
- Term Document matrix(TDM)
- Disadvantages of TDM
- Document Term Matrix(DTM)
- Advantages and disadvantages of DTM
- TF-IDF matrix
- What is TF
- What is IDF
- Word2Vec
- Practical Example using NLP
- Understanding the business case
- Applying NLP on the dataset
- LDA (Topic modelling)
- Measures of Dispersion (Variance, Standard
- Deviation)
- Range, Quartiles, Inter Quartile Ranges
- Measures of Shape (Skewnessand Kurtosis)
- Tests for Association (Correlation and
- Regression)
- Random Variables
- Probability Distributions
- Standard Normal Distribution
- Probability Distribution Function
- Probability Mass Function
- Cumulative Distribution Function

### Inferential Statistics

- Statistical sampling& Inference
- Hypothesis Testing
- Null and Alternate Hypothesis
- Margin of Error
- Type I and Type II errors
- One-Sided Hypothesis Test, Two-Sided
- Hypothesis Test
- Tests of Inference: Chi-Square, T-test, Analysis of Variance
- T-value and p-value
- Confidence Intervals

### Pandas

- Basics of Pandas
- Loading data with Pandas
- Series
- Operations on Series
- Data Framesand Operations of Data Frames
- Selection and Slicing of Data Frames
- Descriptive statistics with Pandas
- Map, Apply, Iterations on Pandas Data Frame
- Working with text data
- Multi-Index in Pandas
- GroupBy Functions
- Merging, Joining and Concatenating Data
- Frames
- Visualization using Pandas

### Matplotlib

- Anatomy of Matplotlib figure
- Plotting Line plots with labels and colors
- Adding markers to line plots
- Histogram plots
- Scatter plots
- Size, Color, and Shape selection in Scatter
- plots.
- Applying Legend to Scatter plots
- Displaying multiple plots using subplots
- Boxplots, scatter_matrix and Pair plots

### Seaborn

- Basic Plotting using Seaborn
- Violin Plots
- Box Plots
- Cat Plots
- Facet Grid
- Swarm Plot
- Pair Plot
- Bar Plot
- LM Plot
- Variations in LM plot using hue, markers, row, and
- columns

### Jupyter Notebook

- Exploratory Data Analysis
- Pipeline ideas
- Exploratory Data Analysis
- Feature Creation
- Evaluation Measures Data Analytics Cycle ideas
- Data Acquisition
- Data Preparation
- Data cleaning
- Data Visualization
- Plotting
- Model Planning & Model Building data

### Inputting

- Reading and writing data to text files
- Reading data from a CSV
- Reading data from JSON Data preparation
- Selection and Removal of Columns
- Transform
- Rescale
- Standardize
- Normalize
- Binarize
- One hot Encoding
- Imputing
- Train, Test Splitting

### Supervised Machine Learning - Classification

- Classification methods & respective evaluation
- K Nearest Neighbors
- Decision Trees
- Naive Bayes
- Stochastic Gradient Descent
- SVM
- Linear
- Non linear
- Radial Basis Function
- Random Forest
- Gradient Boosting Machines
- XGboost
- Logistic regression Ensemble methods
- Combining models
- Bagging
- Boosting
- Voting
- Choosing best classification method Model
- Tuning
- Train Test Splitting
- K-fold cross-validation
- Variance bias tradeoff
- L1 and L2 norm
- Overfit, underfit along with learning curves
- Variance bias sensibility using graphs
- Hyper Parameter Tuning using Grid Search CV
- Respective performance measures
- Different Errors (MAE, MSE, RMSE)Accuracy,
- Confusion Matrix, Precision, Recall

### Supervised Machine Learning – Regression

- Regression
- Linear Regression
- Variants of Regression
- Lasso
- Ridge
- Multi Linear Regression
- Logistic Regression (effectively, classification only)
- Regression Model Improvement
- Polynomial Regression
- Random Forest Regression
- Support Vector Regression Respective
- performance measures

### Different Errors (MAE, MSE, RMSE)

- Mean Absolute Error
- Mean Square Error
- Root Mean Square Error Unsupervised Machine
- Learning Clustering
- K means
- Hierarchical Clustering
- DBSCAN
- Association Rule Mining
- Association Rule Mining.
- Market Basket Analysis using Apriori Algorithm
- Dimensionality reduction using Principal Component
- Analysis (PCA)
- Natural Language Processing
- Text Analytics
- Stemming, Lemmatization, and Stop word removal.
- POS tagging and Named Entity Recognition
- Bigrams, Ngrams, and colocationsTerm Document
- Matrix
- Count Vectorizer

### Term Frequency and Advanced Analytics

- Time series
- Time-series Analysis.
- ARIMA example Recommender Systems
- Content Based Recommendation
- Collaborative Filtering
- Reinforcement Learning
- Basic concepts of Reinforcement Learning Action
- Reward
- Penalty Mechanism Feedback loop Deep Q Learning
- Artificial Intelligence

### Artificial Neural Networks

- Neural Networks& terminologies
- Nonlinearity problem,illustration
- Perceptron learning
- Feed ForwardNetwork and Back
- propagation
- Gradient Descent
- Mathematics of Artificial neural networks
- Gradients
- Partial derivatives
- Linear algebra o Li
- LD
- Eigen vectors
- Projections
- Vector quantization
- Overview of tools used in Neural Networks
- Tensor Flow
- Keras Deep Learning Deep Learning
- Tensorflow & Kerasinstallation
- More elaboratediscussion on cost function
- Measuring accuracyof hypothesis function
- Role of gradient functionin minimizing cost
- function
- Explicit discussion of Bayes models

“Accelerate Your Career Growth: Empowering You to Reach New Heights in Data Science”

## Data Science Training Options

### Data Science Classroom Training

- 50+ hours of live classroom training
- Real-Time trainer assistance
- Cutting-Edge on Data Science Tools
- Non-Crowded training batches
- Work on real-time projects
- Flexible timings for sessions

### Data Science online training

- 50+ Hours of online Data Science Training
- 1:1 personalised assistance
- Practical knowledge
- Chat and discussion panel for assistance
- Work on live projects with virtual assistance
- 24/7 support through email, chat, and social media.

## Data Science Certification Training

The Data Science Course Certification is recognized by a prestigious international organization. We provide you with a Data Science Certification in Chennai and tutor you in the fundamentals of slashing methods.

It improves the value of your CV, and with the aid of this certification, you can land elite positions in the world’s top MNCs.

The certification is only given upon the complete completion of our course and project with a practical component.

## Knowledge Hub with Additional Information of Data Science Training

### Benefits of Data Science

**Increases Business Predictability: ** When a corporation invests in data structuring, it can perform predictive analysis. With the assistance of a data scientist, it is possible to use technologies
such as Machine Learning and Artificial Intelligence to work with the company’s data and, as a result, do more exact analyses of what is to come. As a result, you boost business predictability and may
make decisions now that positively affect your company’s future.

**Ensures real-time intelligence: ** The data scientist can collaborate with RPA professionals to identify their company’s various data sources and construct automated dashboards that search all of
this data in real-time and in an integrated manner.

**Favors the marketing and sales area: ** Data-driven Nowadays, marketing is a generic phrase. The reasoning is straightforward: without data, we cannot create solutions, communications, and products
that are truly in line with customer expectations. As previously demonstrated, data scientists may aggregate data from multiple sources to provide their teams with even more precise insights. Can you
imagine having access to the whole customer journey map, taking into account all of the interactions your customers made with your brand? Data Science makes this possible.

### Future of Data Science in India

According to a recent poll conducted by The Hindu, there are approximately 97,000 data analytics job openings in India due to a shortage of competent individuals. The usage of data analytics in nearly every business has contributed to a 45% growth in total Data Science positions last year. The increasing demand for data scientists provide you with a sense of the breadth of Data Science in India.

### Data Science Payscale

• In most circumstances, an entry-level data scientist has no prior experience in the subject. Their primary focus is usually on learning and practicing skills. Organizations that hire Data Science beginners or amateur data scientists provide them with on-the-job training and preparation.

• The yearly Data Science entry-level compensation is anticipated to be Rs20,000, according to ZipRecruiter. A mid-level data scientist earns Rs30,000 on average.

• A senior data scientist’s pay is one you should aspire for because they earn the highest money out of all of their peers in the same field.

• The median compensation for an experienced data scientist is Rs50,000, according to ZipRecruiter, while the median salary for an experienced manager-level data scientist is Rs84,000.

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### Placement support

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## FAQ of Data Science Course in Chennai

### Why should I join BTree Systems?

BTree Systems is an exponential IT training institute in Chennai and aims to provide every aspirant with ample benefits on their selected DevOps course. We highlight communication, collaboration, and integration-related to IT Management.

### What if I miss the class session?

BTree Systems give records for each data science class so that they can be examined before the following session. BTree Systems’ Flexi-pass offers you access to all or any classes for 90 days, giving you the freedom to choose sessions at your leisure.

### Is there any prerequisite for this course?

Yes, data science entails software and IT sectors, and you need to be familiar with coding, OS – LINUX, and automation. It is preferable to have knowledge of Java, know Knowledge of networking is also required.

### Do you provide course material for post-training?

Yes, we offer lifetime access to data science tools and course materials.

### What is the Data Science Course Duration?

The Data Science Certificate Course is anticipated to take 45 hours. Call us at +91-7397391119 to join that live, interactive discussion.

### How would BTree Systems help me with the placement assistance for the completion of Data Science training?

We have a dedicated placement help team for Data science at our institute. They prepare resumes and assist you in creating a profile on job boards to help you stand out from the throng.

We also work with leading MNCs to place you as a Data Engineer, Data Scientist, Data Architect, Machine Learning Engineer, Business Intelligence Developer, or Database Administrator.

### Can I meet the trainer before joining the course?

We always recommend that students meet with the trainer before beginning the course. Before paying fees, BTree Systems provides a free demo class or a discussion meeting with trainers. We consider you for classes only if you are satisfied with the mentorship of the trainer.

### What should a data science course syllabus include?

The data science program is a broad field in which you learn several outcomes by applying your knowledge and skill set. A reputable Data Science certification program provide you with a solid foundation upon which to enter the sector. It does this by covering Python, machine learning, natural language processing, statistics, and linear algebra.

### What are the eligibility requirements for the Data Science training course?

A clear understanding of fundamental principles in mathematics and statistics, as well as completion of 12 classes and a bachelor’s degree in data science, are requirements for enrollment in the data science course (probability, Calculus, algebra).

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