about course

Precisely, Data Science involves extracting practicable insights from raw data. Various scientific methods, algorithms, and processes are used to extract insights from the wide selection of data. Data Science training & Data Science courses offers a huge set of tools for working with data coming from a different source, such as financial logs, multimedia files, marketing forms, sensors, and text files. An important characteristic of Data Science is the structure of data for analysis, including cleaning, aggregating, and manipulating it to perform advanced analyses. In our Data Science certification course in Dehradun & Uttarakhand you will gain knowledge of well-known machine learning algorithms,python programming basics, principal component analysis, Machine learning concepts and regularisation, Artificial intelligence & Machine learning apllications. Additionally, you will learn about training data and how to use a set of data to identify associations that are unquestionably predictive.

Program hightlight

Instructor-led Classroom and Online Training modes

Designed for Working Professionals & Freshers

Work on live projects

Beginner to expert-Level training

Machine learing with python

Artificial Intelligence & deep learning concepts

Course Details

Python

  • History of Python, features, current industry standards.
  • Basic syntax, data types
  • Control flow statements like - if, else if, if else. Loops - For, While, nested loops Control statements - continue, break, pass Creating Lists, Tuples, Dictionaries
  • Inbuilt functions for Lists, Tuples, Dictionaries. Creating your own functions.
  • Printing star patterns using functions.
  • Creating classes in Python. Accessing class variables, functions using objects. Inheritance in Python. Self, super keyword in python. Creating modules.
  • GUI Introduction. Creating GUI using Tkinter library. Different types of components, like Label, Entry, Button, ScrollView, Canvas etc. to be introduced. Exception Handling introduction.
  • Mini Project - Creating a calculator application.
  • Mini Project - Student Management System using file handling.
  • Database introduction. Updating your restaurant management application to fetch the list of records from database.

What is Mathematical Computing with Python (NumPy) ?

  • An Introduction to the Numpy
  • The Activity-Sequence it Right . Class and Attributes of ndarray
  • All About the Basic Operations
  • Activity-Slice It
  • Copy and Views
  • About the Mathematical Functions of Numpy

The Scientific computing with Python (Scipy)

  • Introduction to the Scipy
  • About the Scipy Sub Package - Integration and Optimization
  • What is Scipy sub package?
  • Know About the Scipy Sub Package - Statistics, Weave and I

The Data Manipulation with Pandas

  • Introduction to the Pandas
  • Understanding DataFrame
  • The Missing Values
  • The Data Operations
  • About File Read and the Write Support
  • What is Pandas Sql Operation?

The Data Visualization in Python using matplotlib

  • Introduction to the Data Visualization
  • What are Line Properties?
  • (x,y) Plot and Subplots
  • The Types of Plots

Machine Learning

  • Formulate a Machine Learning Problem
  • Problem Formulation
  • Framing a Machine Learning Problem
  • Differences Between Traditional Programming and Machine Learning
  • Differences Between Supervised and Unsupervised Learning
  • Randomness in Machine Learning
  • Random Number Generation
  • Machine Learning Outcomes
  • Machine Learning Datasets
  • Structure of Data
  • Terms Describing Portions of Data
  • Data Quality Issues Data Sources
  • Open Datasets
  • Examining the Structure of a Machine Learning Dataset
  • Extract, Transform, and Load (ETL)
  • Loading the Dataset

Use Visualizations to Analyse Data

  • Data Visualizations - Histogram
  • Box Plot
  • Scatterplots
  • Heat Maps using python
  • Guidelines for Using Visualizations to Analyse Data
  • Analysing a Dataset Using Visualizations

Prepare Data

  • Data Preparation
  • Data Types
  • Operations You Can Perform on Different Types of Data
  • Continuous vs. Discrete Variables
  • Data Encoding
  • Dimensionality Reduction
  • Impute Missing Values
  • Duplicates
  • Normalization and Standardization
  • Guidelines for Preparing Training and Testing Data
  • Splitting the Training and Testing Datasets and Labels

Setting Up and Training a Model

  • Hypothesis
  • Confidence Interval
  • Machine Learning Algorithms
  • Algorithm Selection
  • Guidelines for Setting Up a Machine Learning Model
  • Setting Up a Machine Learning Model

Train the Model

  • Iterative Tuning
  • Bias
  • Compromises
  • Model Generalization
  • Cross-Validation
  • k-Fold Cross-Validation
  • Dealing with Outliers
  • Feature Transformation
  • Transformation Functions
  • Scaling and Normalizing Features
  • The Bias-Variance Trade-off
  • Parameters
  • Regularization
  • Models in Combination
  • Guidelines for Training and Tuning the Model

Building Linear Regression Models

  • Linear Regression
  • Linear Equation Linear Equation Data Example
  • Straight Line Fit to Example Data
  • Linear Regression in Machine Learning
  • Linear Regression in Machine Learning Example
  • Matrices in Linear Regression
  • Linear Model with Multiple Parameters
  • Cost Function
  • Mean Squared Error (MSE)
  • Mean Absolute Error (MAE)
  • Coefficient of Determination

Build a Regularized Regression Model

  • Gradient Descent
  • Global Minimum vs. Local Minima
  • Learning Rate
  • Gradient Descent Techniques
  • Building an Iterative Linear Regression Model

Building Classification Models

  • Building Classification Models
  • Train Binary Classification Models
  • Linear Regression Shortcomings
  • Logistic Regression
  • Decision Boundary
  • Cost Function for Logistic Regression
  • A Simpler Alternative for Classification
  • K-Nearest Neighbour (K-NN)
  • Guidelines for Training Binary Classification Model

Train Multi-Class Classification Models

  • Multi-Class Classification
  • Multinomial Logistic Regression
  • Guidelines for Training Multi-Class Classification Models
  • Training a Multi-Class Classification Model

Evaluate Classification Models

  • Model Performance
  • Confusion Matrix
  • Classifier Performance Measurement
  • Accuracy
  • Precision
  • Recall
  • Precision-Recall Trade-off
  • F1 Score
  • Guidelines for Evaluating Classification Models
  • Evaluating a Classification Model

Tune Classification Models

  • Hyperparameter Optimization
  • Grid Search
  • Randomized Search
  • Guidelines for Tuning Classification Models
  • Tuning a Classification Model

Building Clustering Models

  • Build K Means Clustering Models
  • K Means Clustering
  • K Determination
  • Elbow Point
  • Cluster Sum of Squares
  • Guidelines for Building a K Means Clustering Model

Build Hierarchical Clustering Models

  • K Means Clustering Shortcomings
  • Hierarchical Clustering
  • Hierarchical Clustering Applied to a Spiral Dataset
  • Dendrogram
  • Building a Hierarchical Clustering Model

Build Decision Tree Models

  • Decision Tree
  • Classification and Regression Tree (CART)
  • Gini Index/Entropy
  • CART Hyperparameters
  • Pruning
  • One Hot Encoding
  • Decision Tree Algorithm Comparison
  • Decision Trees Compared to other Algorithms
  • Guidelines for
  • Building a Decision Tree Model Building a Decision Tree Model

Build Random Forest Models

  • Ensemble Learning
  • Random Forest
  • Random Forest Hyperparameters
  • Feature Selection Benefits
  • Guidelines for Building a Random Forest Model
  • Building a Random Forest Model

OpenCV

  • Getting Started
  • Reading an image in OpenCV using Python
  • Display an image in OpenCV using Python
  • Writing an image in OpenCV using Python
  • OpenCV Saving an Image
  • Color Spaces
  • Arithmetic operations on Images
  • Bitwise Operations on Binary Images
  • Image Processing
  • Image Resizing
  • Eroding an Image
  • Blurring an Image
  • Create Border around Images
  • Grayscaling of Images
  • Scaling, Rotating, Shifting and Edge Detection
  • Erosion and Dilation of images
  • Analyze an image using
  • Histogram Histograms Equalization
  • Simple Thresholding
  • Adaptive Thresholding
  • Otsu Thresholding
  • Segmentation using Thresholding
  • Convert an image from one color space to another
  • Filter Color with OpenCV
  • Denoising of colored images
  • Visualizing image in different color spaces
  • Find Co-ordinates of Contours
  • Bilateral Filtering
  • Image Inpainting using OpenCV
  • Intensity Transformation Operations on Images
  • Image Registration
  • Background subtraction
  • Background Subtraction in an Image using Concept of Running Average
  • Foreground Extraction in an Image using Grabcut Algorithm
  • Morphological Operations in Image Processing (Opening)
  • Morphological Operations in Image Processing (Closing)
  • Morphological Operations in Image Processing (Gradient)
  • Image segmentation using Morphological operations
  • Image Translation
  • Image Pyramid Working with Videos Getting Started
  • Play a video using OpenCV
  • Video Processing
  • Create video using multiple images
  • Extract images from video
  • Drawing Functions
  • Draw a line
  • Draw arrow segment
  • Draw an ellipse
  • Draw a circle
  • Draw a rectangle
  • Draw a text string
  • Find and Draw Contours
  • Draw a triangle with centroid

Boosting Techniques

  • AdaBoost (Adaptive Boosting)
  • Gradient Tree Boosting
  • XGBoost

Time Series

  • Time series analysis
  • Time series forecasting
  • Stationary time series
  • Non stationary time series
  • Trend component
  • Seasonal component
  • Autoregressive Integrated Moving Average Models
  • Seasonal ARIMA Models
    • ]

Natural Language Processing

  • Text Analytics
  • Tokenizing, Chunking
  • Document term Matrix
  • TFIDF
  • Hands on Sentiment Analysis

Deep Learning

  • Building Artificial Neural Networks
  • Artificial Neural Network (ANN)
  • Perceptron Multilabel Classification Perceptron
  • Perceptron Training
  • Perceptron Shortcomings
  • Multilayer Perceptron (MLP)
  • ANN Layers
  • Backpropagation
  • Activation Functions
  • Guidelines for Building MLPs
  • Building an MLP

Working with TensorFlow ,keras and pytorch

  • Traditional ANN Shortcomings
  • Convolutional Neural Network (CNN)
  • CNN Filters
  • CNN Filter Example
  • Padding
  • Stride
  • Pooling Layer
  • CNN Architecture
  • Generative Adversarial Network (GAN)
  • GAN Architecture
  • Guidelines for Building CNNS
  • Building a CNN

Recurrent Neural Network

  • Sequence Modelling
  • Recurrent Neural Networks
  • Problems with RNN
  • Vanishing and Exploding Gradients
  • LSTM
  • GRU

Auto encoders and decoders

  • Yolo and yolo v3
  • SSD
  • Faster RCNN

Projects To Be Covered

  • Student Management System (Using Tkinter and SQLite Database)
  • 911 Emergency Calls Analysis.
  • India Import and Export Trade data analysis and visualization.
  • Titanic Survival Prediction.
  • Passenger Count Prediction.
  • Human Activity Recognition.
  • Employee Attrition Prediction and Model Deployment.
  • Recommending movies based on previous user data.
  • Recognizing faces from the video.
  • Finding number plate from images of vehicles and saving them to database
  • Extracting dates from data.
  • Predicting sentiment from user reviews.
  • Managing store accessories and analyzing customer data for different regions and products.
  • Extracting all the images and book names, title, price and genre using automation.

Get In Touch

Who Can Apply ?
Job Roles
Business Analyst
To understand the data in the database & applications and ustilize the data for the business growth and company profit.
Data Mining Engineer

As a Data Mining engineer you will be accountable for both Database & its manager, you will be the adviser for the equipments, software & application that are required for the improvement of company future and its benefits.

Data Architect

Data Architect designs & model the database solution system to store the company data, the Data Architect does the examination to identify the prerequisites for database structure by evaluating client operations, applications, and programming

Data Scientist

Data Scientist gather useful information from the unstructured data so that it can be used for futher Application.

Senior Data Scientist

A senior Data scientist is highly qualified who adopt leading technologies in order to get high performance computation for the Organization with providing data storage soloutions.

Senior Data Architect

A senior Data architect eliminate the complicated data system's unrelated cost to simplify the flow of data in the organization.

Tools To be Covered
Meet the Mentor

Shubham Soni

Data science Training Consultant An Industry Experienced Data science Expert having a working knowledge of all Analytics Tools & Technologies. He has trained over 400 Candidates in various Data Analytics Tools.

Prakash Kandpal

Data science Training Consultant Experienced instructor in the domain of Data Analytics, Cyber Security & Networking. He has trained over 1000 candidates & holds multiple certifications.

Student testimonials

Ronak Dua

Data science

Undoubtedly you provide the best Data Science course, I was already impressed in the demo classes and the trainers are so experienced and helping that you will never feel confused and left out, currently i am placed at ML Technologies all because of Brillica services, thanx A ton!

Aayush Rawat

data science

My overall experience at Brillica was up to the mark. The trainers/teachers are well versed with the topics, and are quite supportive and helpful. Anyone who wants to brush up their basics or enhance their skills should join here, they provide the best Data Science course in Dehradun & Uttarakhand.

Suman Pandey

Data science

I joined Brillica to take the training of Data Science I enjoyed the course and learned a lot from it. The content is well organised and focused on practical situations. They also provide the recording which is excellent for revising. The courses in Brillica services are so informative and delivered in a way that is clear and concise.

Srishti Singhal

data science

Really nice experience. I was enrolled in Data Science program and liked the way teachers concentrate on every child even in online classes. Great faculty who supports you even after completion of course. You will not regret investing your money and time here. Just do all the assignments told and you will be good to go. Thanks.

Tech World 2021

data science

I did IOT and Data Science from here . Very nice coaching is really helping me in my carrer . Class mai hasi mazak bhi hota hai . I would highly recommend this place for IOT and Data Science . Enroll today.

Shivam Amoli

data science

Opted for Data science using Python, and it was a great start. All concept were explained by Gurjas sir in every possible way. All doubts cleared. Had a very good experience learning.

Anmol Jaiswal

data science

It was best 6 months spent here to learn data Science skills.

Naincy Prabha

data science

I am from uttranchal University, Uttrakhand.I had a great experience in learning here.
Our Alumni Work At
Career Services
Brillica Services provides 100% job placement support to all the candidates. Brillica Services offers lifetime multiple interview sessions based upon the skills and submission of the projects after the completion of 50% of the course. We provide the latest hiring updates from the companies with our students so that they can apply for it accordingly so do not delay furthur and join our Data Science course with 100% placement Assistance.
Students are prepared for interviews after the completion of 50% of the course which includes assesments practical training, we know what industry requires now so we train our candidates accordingly and focus on the tools which are important.
We help students in profile building and assist them in making an impressive resume with their strong skills highlighted. Students get to train accordingly to the companies requirement and also we focus on the relevant skills that is required by the IT industries right now.
Certification

Data Science certification in Dehradun, Uttarakhand helps you to get ready for the most desirable career domain. We provide courses in multiple domains which specifially focused on IT, the Technology field is growing very fast and right now there are ample of opportunities for everyone, the positive aspect is that if ypu don not have the IT background but you have the skills requried according to the industries then there is no stopping you, make your career future proof and get upgraded according to the need of time.

Projects Delivered

Sentiment Analysis

Detecting Parkinson's Disease

Detection of Fake News

Next word prediction

Movie Recommender

Customer Segmentation

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FAQs

1. What are the objectives of data science?

The objective of data science is to assemble the means for extracting business-focused insights from the data. This requires an understanding of how value and information proceeds in a business and the potentiality to use the understanding to recognize the business opportunities. Examples of data science are credit card fraud monitoring solutions used by banks, or tools used in optimizing the placement of wind turbines in wind farms.

2.Why to become a data scientist?

Data scientists are the specialists who use their expertise in both the technology and social science to come across drifts and manage the data. They utilize industry information,circumstantial understanding, apprehension of existing assumptions – to uncover answers for business challenges. Employed as a data scientist can be intellectually challenging as well as analytically satisfying.

3. What important skills you will learn in the data science training course?

Data Science training course in Brillica Services offers a huge set of tools for working with data coming from a different source, such as financial logs, multimedia files, marketing forms, sensors, and text files. An important characteristic of Data Science is the structure of data for analyses, including cleaning, aggregating, and manipulating it to perform advanced analyses.

4. Who can apply for the data science training course?

Anyone can apply for the Data Science training course like IT graduates including BCA, MCA, BBA, and MBA, Btech, Mtech, B.Sc(IT), M.Sc(IT) graduates and PhD holders. We provide the best opportunities for all working professionals also with live Data Science projects in Dehradun, Uttarakhand and Delhi.

5. what are the pre-requisites for the data science certification training?

It is very important to understand why some people are very good in Data science domain and others cannot, though having a good knowledge of data science. Some important points one should have knowledge of about before diving into the data science domain are:
1.Knowledge of Data
2.Knowledge of algorithms/logic
3.Knowledge of programming languages
4.Knowledge of Statistics
5.Knowledge of business domain
Of the above-described points, if anyone is not aware of anyone one of them, it is better to have a good knowledge about those concepts first and then think to start the data science course.

6. What are the opportunities after the completion of data science training course?

Some of the most demanding roles for data science aspiring candidates are:
1. Business Intelligence Analyst
2. Data Mining Engineer
3. Data Architect
4. Data Scientist
5. Senior Data Scientist

7. What opportunities does Brillica Services provide?

Brillica Services provides 100% Job Assistance guarantee to the students who aims to gear up their career in various trending sectors. Brillica Services also prepare students for the interview questions trending in the respective fields.

8. Why choose Brillica Services for Data Science Training Course?

Brillica Services provides the best project based Data Science certificate training course in Dehradun, Uttarakhand and Delhi. Data Science course is suitable for any IT graduates having basic understanding of mathematics and statistics. Although it is not mandatory but having knowledge will be more beneficial. Brillica Services provides the best online and offline courses with certificate in Data Science.

9. Does Brillica Services helps in the placement or internship?

Yes, of course. Brillica Services provides 100% job assistance guaranteed to all its students. We help in the placement process and also provides internship. Our all the courses are industry certified and real scenario-based approach is followed during training course.