Advanced Data science training with Free Internship & 100% Placement Assistance

About the Data Science Course Training in Hyderabad

Data is everywhere, which is growing exponentially globally, and this can still grow at an accelerating rate for the foreseeable future. Businesses generate massive amounts of data in the form of blogs, messages, transaction documents, mobile device data, social media, etc. By using this data effectively, a business firm can create vital value and grow their economy by enhancing productivity, increasing efficiency, and delivering more value to consumers.

Data Science helps in combining the disruption into categories and communicating their potential, which allows data and analytics leaders to drive better results. Top businesses thought there is a necessity to analyze the data for significant benefits. They use the insights from data for the benefit of users.

Human decision making is becoming increasingly inadequate to pander to a never-ending expansion of the data. However, Data Science and Machine Learning are excelling in solving highly complex data-rich problems. To think beyond the human brain and maintain the balance with the information that’s evolved, disrupted, and being employed the sectors altogether, data scientists foster new methodologies. Data scientists must try ‘big data expeditions’ to explore the data for previously undiscovered value – the first common application of data science. Typical applications include marketing segmentation, advertising, tweaking dynamic pricing models, or banks finding risks and adjusting the financial risk models.

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What are the Tools used in Data Science?

What are the Tools used in Data Science?

What is the scope of Data Scientists in India?

In this Data eruptive field, businesses need a head who owns a brain that is good at maths, finesse, the eyes of an artist, and more.

Data Science is quoted as the Sexiest Job of the 21st Century by Harvard Business Review

There is a severe shortage of Data Scientists with excellent analytical skills and deep quantitative abilities who can analyze big data across all industries. The method of realizing questions, data, product use cases are done by applying their curiosity, quantitative skills, and intellect toward understanding big data are now called data scientists. This most in-demand position, therefore, businesses are in dire need of the people who can solve complex challenges and foster growth.

What are the modes of Data Science course training at Hyderabad?

We provide classroom training on Data Science in Hyderabad for the individuals who believe hand-held training. We teach as per the Indian Standard Time (IST) with In-depth practical Knowledge on each topic in classroom training, 80 – 90 Hrs of Real-time practical training classes. There are different slots available on weekends or weekdays according to your choices. We are also available over the call or mail or direct interaction with the trainer for active learning.

For any queries feel free to call/whatsapp us on +91-9951666670 or mail at info@innomatics.in

We provide Online Digital Marketing Training for the individuals who are occupied with work and the person who believes in one-one learning. We teach as per the Indian Standard Timings, feasible to you, providing in-depth knowledge of Digital Marketing. We are available round the clock on WhatsApp, emails, or calls for clarifying doubts and instance assistance, also giving lifetime access to self-paced learning.

For any queries feel free to call/whatsapp us on +91-9951666670 or mail at info@innomatics.in

We provide Data Science training for corporates by experts, which helps businesses to strengthen and reap huge benefits. We always stay updated and provide training on real-time use cases, which bridges the gap enabling the organization to capitalize on the potential of the employees.

For any queries feel free to call/whatsapp us on +91-9951666670 or mail at info@innomatics.in

Data Science Batch details

Data Science New Batch Details

Schedule/Day Date Timings
Weekday Batch (MON-FRI) 04/05/2020  8AM, 10 AM, 7PM
Weekend Batch (SAT-SUN) 02/05/2020 10 AM
Online Batch 13/04/2020 8AM, 10AM, 7PM
Student Program N/A N/A

Current Available Slots

Timings Total Seats Available Seats
8AM (MON-FRI) 15 5
10AM (MON-FRI) 30 (2 classrooms) 10
7PM (MON-FRI) 15 8
10AM (SAT & SUN) 30 (2 classrooms) 12
ONLINE – 6AM,7PM 20 10

Note: Demo On Every Saturday & New Batch Will Starts On Every Monday

Data Science Course Curriculum

1. Introduction to Data Science

  • Introduction to data science
  • Application of Data Science
  • Life cycle of data science projects
  • Data and its various forms

2. Python - Core, Advanced

1. INTRODUCTION 

  • What is Python?
  • Why Data Science requires Python?
  • Installation of Anaconda
  • Understanding Jupyter Notebook
  • Basic commands in Jupyter Notebook
  • Understanding Python Syntax

2. Data Types and Data Structures

  • Variables
  • Strings
  • Lists
  • Sets
  • Tuples
  • Dictionaries

3. Control Flow and Conditional Statements

  • Conditional Operators, Arithmetic Operators and Logical Operators
  • If, Elif and Else Statements
  • While Loops
  • For Loops
  • Nested Loops
  • List and Dictionary Comprehensions

4. Functions

  • Code Optimization
  • Scope
  • Lambda Functions
  • Map
  • Filter
  • Reduce
  • Modules and Packages

5. File Handling

  • Create, Read, Write files
  • Operations in File Handling
  • Errors and Exception Handling

6. Python Datatime

7. Object Oriented Programming(OOP)

  • Attributes and Class Keywords
  • Constructers and Destructors
  • Using Self Parameter
  • Class Object attributes and Methods
  • Overloading and Over Riding
  • Inheritance
  • Method Overloading
  • Operator Overloading
  • Abstraction
  • Super Keyword
  • Method Overriding

3. Python for Data Analysis & Visualization

8. Numpy 

  • Arrays
  • Basic Operations in Numpy
  • Indexing
  • Array Processing

9. Pandas

  • Series
  • Data Frames
  • Indexing and slicing
  • Groupby
  • Concatenating
  • Merging Joining
  • Missing Values
  • Operations
  • Data Input and Output
  • Pivot
  • Cross tab

10. Data Visualization

  • Introduction to Matplotlib
    • Line plots
    • Histograms
    • Box and Violin Plots
    • Scatterplot
    • Heatmaps
    • Subplots
  • 11. Visualization with Seaborn

4. Understanding Text using Python

12. Regular Expressions

  • Literals and Meta Characters
  • How to Regular Expressions using Pandas?
  • Inbuilt Methods
  • Pattern Matching

 

Projects

  • Data Mining

This project starts completely from scratch which involves collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models. This project covers the main four steps of Data Science Life Cycle which involves

  1. Data Collection
  2. Data Mining
  3. Data Preprocessing
  4. Data Visualization.

Ex: Text, CSV, TSV, Excel Files, Matrices, Images

5. Statistics - DESCRIPTIVE & INFERENTIAL

Basic Statistics Terminology

  • What is Statistics?
  • How Statistics is used in Data Science
  • What is Probability?
  • Population and Sample
  • Sampling Techniques
    • Convenience Sampling
    • Simple Random Sampling
    • Systematic Random Sampling
    • Stratified Sampling
    • Cluster Sampling
    • Variables
      • Dependent and Independent Variables
      • Qualitative and Quantitative Data
        • Categorical Data
          • Nominal
          • Ordinal
        • Numerical Data
          • Interval
          • Ratio
        • Discrete and Continuous Data

      Central Tendencies

      • Mean, Median and Mode
      • Standard Deviation and Variance
      • Box Plot and Distribution

      Basics of Probability

      • Probability vs Statistics
      • Terminology
      • Probability Rules
      • Probability Types
        • Marginal Probability
        • Joint Probability
        • Union Probability
        • Conditional Probability

      Probability Theory

      • Conditional Probability
      • Bayes Theorem
      • Confusion Matrix
      • Z-Score
      • Histogram

      Probability Distribution

      • Expectation
      • Variance of Distribution
      • Skewness
      • Kurtosis
      • Discrete Probability Distribution
        • Bernoulli
        • Binomial
        • Geometric
        • Poison
      • Continuous Probability Distribution
        • Exponential
        • Normal Distribution
        • Gaussian Distribution
        • t-Distribution
        • Confidence Interval
          • Standard Error
          • Margin of Error

          Statistical Testing

          • Hypothesis Testing
          • Chi-square test
          • t-test
          • ANOVA

6. MACHINE LEARNING – SUPERVISED LEARNING

1. INTRODUCTION

  • What is Machine Learning?
  • Difference between Supervised Learning and Unsupervised Learning?
  • Difference between Regression and Classification Models?

2. Linear and Multiple Regression

  • Relationship between variables: Regression (Linear, Multivariate Linear Regression) in prediction.
  • Hands on Linear and Multiple Regression using a use case.
  • Understanding the summary output of Linear Regression
  • Residual Analysis
  • Identifying significant features, feature reduction using AIC, multi-collinearity check, observing influential points, etc.
  • Hypothesis testing of Regression Model
  • Confidence intervals of Slope
  • R-square and goodness of fit
  • Influential Observation – Leverage
  • Polynomial Regression
  • Categorical Variable in Regression

3. Logistic Regression

  • Logistic Regression Intuition
  • Understanding Logit Function.
  • Hands-on Python Session on Logistic Regression using business case.
  • Measuring the Evaluation Metrics – Confusion Metrics, Accuracy, Precision, recall and ROC Curve.

4. Navie Bayes Classifier

  • Review probability distributions, Joint and conditional probabilities
  • Model Assumptions, Probability estimation
  • Required data processing
  • Feature Selection
  • Classifier

5. Principal Compound Analysis (PCA)

  • Introduction to dimensionality reduction and it’s necessity
  • Background: Eigen values, Eigen vectors, Orthogonality
  • Principal components analysis (PCA)
  • Feature Extraction
  • Advantage and application of Dimensionality reduction.

6. Time Series (Forecasting)

  • Trend analysis
  • Cyclical and Seasonal analysis
  • Smoothing; Moving averages; Auto-correlation; ARIMA
  • Application of Time Series in financial markets

7. Decision Tree (Rule – Based)

  • Decision nodes and leaf nodes
  • Variable Selection, Parent and child nodes branching
  • Stopping Criterion
  • Tree pruning and Depth of a tree
  • Overfitting
  • Metrics for decision trees-Gini impurity, Information Gain, Variance Reduction
  • Regression using decision tree
  • Interpretation of a decision tree using If-else
  • Pros and cons of a decision tree
  • Accuracy estimation using cross-validation 

8. K-Nearest Neighbor (Distance Based Learning)

  • What is KNN and why do we use it?
  • KNN-algorithm and regression
  • Curse of dimensionality and brief introduction to dimension reduction
  • KNN-outlier treatment and anomaly detection
  • Cross-Validation
  • Pros and cons of KNN

9. Support Vector Machine (Distance Based Learning)

  • Linear learning machines and Kernel space, making kernels and working in feature space
  • Hands on example of SVM classification and regression problems using a business case in Python.

10. Esemble Methods

  • Introduction to Ensemble
  • Bias and Tradeoff
  • Bagging & boosting and its impact on bias and variance
  • Random forest
  • Gradient Boosting
  • XGBoost

Case Studies:

  • Predictive Analytics
  • Banking Use cases – Customer Service prediction,
  • Health care Use cases – Heart Disease, Diabetics
  • Insurance Use cases
  • Telecom Churn Prediction
  • Bike Sharing
  • Air Quality

7. MACHINE LEARNING – UNSUPERVISED LEARNING

1. Clustering

  • Different clustering methods
  • review of several distance measures
  • Iterative distance-based clustering
  • Dealing with continuous, categorical values in K-Means
  • Constructing a hierarchical cluster, and density-based clustering.
  • Test for stability check of clusters
  • Hands-on implementation of each of these methods in Python

2. Recommendation Systems

  • Association Rules:
    • How to combine clustering and classification;
    • A mathematical model for association analysis
    • Apriori: Constructs large item sets with mini sup by iterations
    • Metrics of rules-Lift, Support, Confidence, Conviction
  • Recommendation Rules:
    • Collaborative Filters
    • Content based Learning

8. Natural Language Processing (NLP) – Text Mining

1.  INTRODUCTION

  • What is Text Mining?
  • Libraries
    • NLTK
    • Spacy
    • TextBlob
  • Structured and Unstructured Data
    • Extracting Unstructured text from files and websites

2. Text Preprocessing

  • Regular Expressions for Pattern Matching
  • Text Normalization
  • Text Tokenization
    • Sentence Tokenization
    • Word Tokenization
  • Text Segmentation
    • Stemming
    • Lemmatization

3. Natural Language Understanding (NLP Statistical)

  • Automatic Tagging
  • N-grams Tagging
  • Transformation based Tagging
  • Bag of Words
  • POS Tagging
  • TF – IDF
  • Cosine Similarity
  • Thinking about the math behind text; Properties of words; Vector Space Model
  • Named Entity Recognition
  • Relation Extraction

4. Matrix Factorization

  • Singular Value Decomposition

5. Text Indexing

  • Inverted Indexes
  • Boolean query processing
  • Handling phrase queries, proximity queries
  • Latent Sematic Analysis

6. Text Classification

Case Studies:

  • Text Mining
  • Sentiment Analysis
  • Spam Detection
  • Dialogue Prediction

9 Artificial Intelligence

1. Introduction to Neural Networks

  • Introduction to Neural Network
  • Introduction to Perceptron
  • Activation Functions
  • Cost Functions
  • Gradient Decent
  • Stochastic Gradient Descent
  • Back propagation

2. Deep Frameworks

  • Installing Tensorflow and Keras
  • Tensorflow and Keras Basic Syntax
  • Tensorflow Graphs
  • Variables and Placeholder
  • Saving and Restoring Models
  • Tensorboard

3. Artificial Neural Network with Tensorflow

  • Neural Network for Regression
  • Neural Network for Classification
  • Evaluating the ANN
  • Improving and tuning the ANN

4. Convolution Neural Networks

  • Convolution Operation
  • ReLU Layer
  • Pooling
  • Flattening
  • Full Connection
  • Softmax and Cross Entropy

5. Building Convolution Neural Network in Python

  • Introduction to Computer Vision
    • OpenCV library in Python
  • Getting Started with Images/Videos
    • Operations on Images
  • Image Processing in OpenCV
    • Geometric Transformation of Images
      • Rotation
      • Affine Transformation
      • Perspective Transformation
    • Imaging Thresholding
    • Contours
    • Edge Detections
    • Morphological Transformation
    • Harris Corner Detection
  • Reshaping Images
  • Normalizing Images
  • Building Convolutional Network with Tensorflow
  • Training CNN for Image Classification

Case Studies:

  • Image Classification

6. Keras (Backend Tensorflow)

  • Keras vs Tensorflow
  • Introduction to Keras
  • Building Artificial Neural Network with Keras
  • Building Convolution Neural Network with Keras

7. Natural Processing Language (Sequential Process)

  • The Idea behind Recurrent Neural Networks
  • Vanishing Gradient Problem
  • LSTM (Long Short-Term Memory)
  • GRU (Gated Recurrent Unit)

 Projects

  • Face Recognition

Face Recognition project gives details of the person and can recognize the gender and names. This project involves in

  1. Collection of images
  2. Preprocessing the data
  3. Applying the Model (Machine Learning or Deep Learning)
  4. Training and Testing using the model

Ex: Security Unlock, Gender Recognition, Identity Recognition 

  • Chatbot

Virtual Assistants are now a common requirement for an Organization. But, to make the assistant more effective we are now into the chatbots which involves Natural Language Process, Deep Learning and Artificial Intelligence. This interactive chatbots are designed to serve as an intellectual responsive process.

               Ex: Alexa, Siri, Google Assistant

10 Deployment

  • Creating pickle and frozen files
  • Cloud Deploying Machine Learning and Deep Learning model for production
Growing Demand
Harvard Business Review About Data Science

Job opportunities (Careers) in Data Science

Data Scientists are needed for businesses in every Industry. Even fortune companies as Google, Amazon, Apple, Facebook, Microsoft need data science experts who have in-depth knowledge of data extraction, data mining, visualization, and more. Some of the leading data science careers are

  • Business Intelligence Developer, with an average salary of $89,333, to design and develop business strategies for quick decision making and growth.
  • Data Architect, whose average salary is $137,630, builds data solutions that can be applied on multiple platforms.
  • Applications Architect, whose average salary is $134,520, tracks applications behavior and applied in the business to analyze the way they interact with the user.
  • Industry Architect – with an average salary of $126,353, to analyze the business system and optimize accordingly to support the development of updated technologies and system requirements.
  • Enterprise Architect with an average salary of $161,323 to work with stakeholders, including management and subject matter experts (SME), to develop a view of an organization’s strategy, information, processes, and IT assets.
  • Data Scientist, with an average salary of $139,480, to explore, analyze, visualize, and organize data for the companies. They analyze the complex data sets and process to find patterns for decision making and predicting the business and drive strategies.
  • Data Analyst- with an average salary of $83,989 to transform and manipulate large sets of data, which incorporated web analytics tracking and testing.
  • Data Engineer – with an average salary of $151,498, to perform real-time processing on data that is visualized and stored.

Meet Our Expert Advisors

Why Data Science at Innomatics Research Labs?

  • 18+ Data Science Industry experts from fortune 500 companies
  •  Dedicated In-house data scientist team accessible round the clock
  •  200+ Hours of intensive practical oriented training
  •  Data science Internship on our projects & products
  •  30+ POCs and use cases to work, learn and experiment
  •  200+ professionals trained in the last three months
  •  5+ products already developed and ready to be deployed
  •  Eight parallel Data science batches running currently on both weekdays and weekends
  •  Bi-weekly Industry connects from industry experts from various sectors 
  •  Project and use-cases derived from businesses
  •  Opportunity to participate in meet-ups, hackathons, and conferences
  •  Dedicated training programs for NON-IT professionals
  •  New batch on every Monday and Saturday for working and non-working professionals
  •  100% placement assistance

Capstone Projects

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A few reviews from our students

Vidyasagar Chakrapani

Dear friends, initially I struggled a lot to find the best training centre for Data Science in Hyderabad. Finally, I ended up joining Innomatics after a lot of research. Initially, I was a little worried about whether I will be able to understand Data Science since I come from a non-programming and non-technical background. But to my surprise here the trainers and the mentors are so cooperative that I never felt that I come from a non-technical background. Here the quality is top-notch and the way treat every student is simply superb. They have assigned Eswar as a mentor to our group right from the beginning and he was always there for us to clarify the doubts and helped us with the projects. And from the past 3 weeks, they have been conducting interview preparation sessions in every afternoon for 3 hours. They conduct the hackathons, quizzes, assignments and mock interviews. Since they are also into development we are also having access to real-time projects. Thanks a lot to Innomatics and I definitely recommend this to everyone.

Data Science Trainee

Sai Chaitanya

I joined Innomatics as a student and an Intern at the end of December 2018. It has been a knowledgeable experience to date. I got to work on some interesting projects and case studies as a Data Science Intern. The mentors at the institute are talented, experienced, yet friendly. They are always ready to help. It has been a great experience and I am sure I will benefit a lot from it in my career.

Data Scientist

Durga Bhavani

I am from Guntur Nalanda college ECE department. I searched almost all the institutes in HYD for data science. I attend a workshop in innomatics. They r teaching is completely industry-related They also have an in-house development team. After the course, these will offer paid internship I am doing the coding. I made a ✔choice of choosing innomatics Thanks to management. If u r NON-IT background I recommend innomatics to everyone.

Data Science trainee

Vakkalagadda Rajasree

I am BTech fresher When I am looking for data science training institutes. I found innomatics is so practical The way they teach everything is practical 1st-week stats From 2nd week they will start python programming. Any layman can understand, They cover from basics to advance, they have nearly 220 +uscases. This is only institutes who train on web scraping, projects These people will cover Python, Statistics, Machine learning SQL, Excel, NLP, Deeleaening, Finally I am done with course. I am doing the coding. If u r from NON-IT blindly joins innomatics. Thanks to innomatics. I made the right choice.

Data Science Trainee

Sai Charan

I never regret joining this institute, first things first the atmosphere over the institute is pleasing which creates the study environment. Sessions in the classes are quite interesting and made me learn more. Yes, the projects given by the institute is more in number but this boosted me even I’m from the EE background now I can code and apply machine learning skills. Especially I thank the faculty in the institute for helping me out with job opportunities. I highly recommended Innomatics 🙂

Data Scientist

Dipti Thakkar

I found the courses at Innomatics Research Labs extremely practical and market-oriented. It was a great and high-quality learning experience in terms of Instructor’s knowledge, delivery, course content of the program, notes, quality of materials and especially the support staff for handling questions & queries right on time. I have completed the program – it more than met my expectations and I feel I have really benefited from it. It was one of the best decisions that I made in my life. Data Science Program here bridges the gap between theory and practical which is very much required in the industry. I highly recommend this course with my full conviction & sincerity to everyone who wants to start a career in DATA SCIENCE Thank you very much, Innomatics Team, for helping me start my Data Science Career.

Data Science Trainee

Rishabh Garg

Writing this review after completing close to 3 months at this institute: I am working as a Tech Lead for Oracle Apps at a major Indian Tech firm for their client projects in Asia Pacific Countries. I came from Sydney to Hyd for a short project and decided to utilize spare time to learn Data Science. Before joining Innomatics, I researched a lot of online and classroom training for Data Science. I even attended a couple of demo sessions of many institutes. Finally, I choose Innomatics because of the depth and clarity of the trainers as well as the professional environment. All main trainers have rich industry exp n they also have an in-house team who r building products on image processing and NLP, they also absorb good performers into this inhouse team. In the last 3 months, able to complete 3 major modules: Stats, Python and Machine Learning – Regressions, which were greek to me until now. Thanks to Arvind, Avinash, Amresh, Raghu. Appreciate the fact that they extend their super tight schedules to help out whenever required. Unlike other places, here some trainers went out of their way and also sacrificed their weekends many times to provide more support for difficult topics whenever required like Hypothesis-testing in Stats, PCA and LogReg in ML, etc… Recommend this institute to anyone looking to start their journey into data science.

Data Science trainee
Oracle Apps

Vaishnavi Mandhani

Innomatics Research labs are the only platform to learn Data Science with real-time working experience, they just don’t teach, let you work and gain experience. There are so many institutions charging Lakhs of rupees but not teaching complete data science. But at Innomatics, I learnt the real data science and practical application of it. After joining here only I got to know what real Data science is. 3 months back I don’t know Data science. Today I can claim that I am an expert of it. As part of my internship, I worked on Docnizer project. It gave me an enormous amount of knowledge in Data Science. Thank you Innomatics – Kukatpally Branch

Data Scientist

Vinay Keerthi

Innomatics Research Lab is one of the best data science training institute in Hyderabad. I joined here as soon as I completed my bachelor’s to attain a great job. This team suggested me to go with a data science course, for the first two days I heard the demo and just got impressed by their teaching and with no second thought choose this course. I’m almost about to complete my course and no doubt they are Turing me into very well qualified data scientist.

Data Science trainee

Patlolla Laxma Reddy

This is my first offline course ever taken. Initially, I was confused to take the Data Science Associate Course offline but, accidentally, I came across INNOMATICS Research Labs website after inquiring about the course from many sources. After seeing the course curriculum and way of teaching, it impressed me. Starting from the first day of sessions, I was very happy and impressed with the way they handled the classes. Never did I feel bored during the sessions and the hands-on experience is good. The credit for this experience goes to Innomatics and the educator. I hope to travel with Innomatics for few more courses(Recently Launched INVESTMENT BANKING) in the days to come. Important Note: They did not make any false commitments/promises that we provide 100% Job guarantee, in this case, so many other institutes give you false commitment(100% job) but they never achieved their commitments. All they have promised me that provide the best training. Thanks for this wonderful experience!

Data Science Trainee

PV Meenakshi

One of the best learning institute in Hyderabad I say! In every aspect, Innomatics research labs Institute has proved to be the best for me. The topics and projects covered in Machine Learning were real-time and it helped me understand the basics along with high-level stuff, equally well. Thank you Innomatics for turning me into a Young data scientist!

Data Scientist

Frequently Asked Questions (FAQs)

1) What will I learn in Data Science?

In Data Science, you will learn how to find valuable data, analyze and apply mathematical skills to it to use in business for making great decisions, developing a product, forecasting, and building business strategies. 

2) What is the average salary of a Data Scientist?

Salary of a Data Scientists entirely depends on the skillset. As per the recent reports, on average a Data Scientists earn ₹14,00,000 per year.

3) Are there any prerequisites to learn the Data Science course?

One need not have any major knowledge in Data Science. A basic understanding of technology is all enough to get started. It is better to possess knowledge of mathematical and communication skills, Python, R, and SAS tools.

4) What are my takeaways after completion of the Data Science course?

Based on the program you choose, you will get a course completion certificate from Innomatics. Mastery level certification from IBM.

5) What are the career opportunities in Data Science?

As data has become the never-ending part of this world, businesses need people to work with data for effective business processing. Organizations are ready to recruit and pay top dollars to the right dollars, which can leverage the business.

Here are some of the roles you can find in Data Science

  • Research Analyst
  • Data Scientist
  • Data Analyst
  • Big Data Analytics Specialist
  • Business Analyst Consultant / Manager
  • Data analyst

6) Do you provide any placements if I learn the Data Science course at Innomatics?

Apart from the training, we do provide placement and career assistance with capstone projects and hands-on training after completing the course successfully. We do offer internship programs, mockup interviews, hackathons to gain more knowledge and explore a wide range of job opportunities.

7) Will I get any career support after the training?

All our trainees will have access to the Learning Management System (LMS), where they can get the backup classes and stay updates, 1-1 interviews, continuous updates on placements, and hackathons.

8) What is the eligibility criteria to learn the Data Science course?

Anyone who has a bachelor’s degree, a passion for data science, and little knowledge of it are eligibility criteria for the Data Science Course.

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