Data Science Course in Chennai

1800+
10+
80 to 100 Hrs

Data Science Training in Chennai at Credo Systemz providing by experienced Data Scientists. Our Data Science Course in Chennai module is completely structured about how to analyze Big Data using R programming and Hadoop. Credo Systemz is the Best place to learn Data Science with Python Training in Chennai. Our Data Science certification course in Chennai will makes you a professional Data Scientist. If you really Interested to Learn Best Data Science course in Chennai, then Credo Systemz is the Right place.

Our Data Science Training in Chennai kick starts from statistics and insights of the large volume of Data. During our Data Science Course, you will be practice many real world practicals and more assessments. It will makes you stronger in Data Science skills. At the end of the course, you become a Data Scientist. So that we ranked as Best Data Science Training Institute in Chennai, Velachery and OMR.

About Data Science Course in Chennai

Before enrolled into our Best Data Science Course in Chennai, just have a glance about Data Science, its purpose, certification, etc., Feel free to reach us to get more knowledge about it and clear your any queries related to Data Science.
Data Science is a knowledge of various fields which consists of planning, methods, process and extracts the knowledge of the system or idea from the data which is in multiple formats that might be organized or unorganized like Data Mining. Data Science really very useful for managing large sets of data from the huge level of businesses which is using various algorithms and analysis. Essentially Data Science identifies the data insight. Data Science Explanation
The Internet has an Incredible easy way to gather any kind of information. Even you can get an idea about how to design your own flight šŸ™‚ Yes, we can get whatever we want. But the subject is how we are going to use these data efficiently?If we want to succeed, we must be adopted and updated to the latest trends and technologies. In Todayā€™s world, everything depends on the data, from Tea shop to Multi-National companies relying upon their data. Now we understand that Data is very essential, so we have to start analyzing. The major role of Data Scientist is to analyze their companyā€™s data and determines good tactics for their company future according to the current situation of the market. Sounds good?
  • Data analytics is the heartbeat of all top organizations
  • Data Science is everywhere
  • You become Decision-Making person
  • Data Science is rapidly increasing than expected
  • Offering new revenue strategies
Know here the Skills You Should have to be a Data Scientist.
The role of a Data Scientist plays a vital role in an organization and of course it differs from company to company, the below are the list of important roles of an organization,The role of a Data Scientist plays a vital role in an organization and of course it differs from company to company, the below are the list of important roles of an organization,
  • Assists management to make better decisions
  • To define organizational goals and strategies
  • Helps the staff to adopt the best practices
  • To identity the growth opportunities
  • Helps to spot the target audience
  • Data Analysis and Data Cleaning
  • To act as an statistician Assists management to make better decisions
  • To define organizational goals and strategies
  • Helps the staff to adopt the best practices
  • To identity the growth opportunities
  • Helps to spot the target audience
  • Data Analysis and Data Cleaning
  • To act as an statistician
    • Harvard Business reviews calls Data Science as the sexiest job of the 21ā€™st century, the job roles of a Data Scientist differs from one to another organization according to their domain. The below are the role of Data Scientist among companies,
    • Data Scientist
    • Data Architecture
    • Business Analyst
    • Data Engineer
    • Data Analyst
    • Data Administrator
    • Statistician
    • Data and Analytics Manager
    • IBM
    • Amazon
    • Facebook
    • Numerator
    • Cloudera
    • Splunk
    • Google
    • Uber
    • Apple
    • Oracle
    • Teradata

    Data Science Course Key Features

    Training from
    Industrial Experts

    24 x 7
    Expert Support

    Hands on
    Practicals/ Projects

    Certification
    of Completion

    100% Placement
    Assistance

    Free
    Live Demo

    DATA SCIENCE TRAINING COURSE CONTENT

    What you will get in the Course?

    • Machine Learning Algorithms /Supervised
    • Linear Regression & Polynomial Regression
    • Random Forest & NaĆÆve Bayes
    • SVM, GBM, Xgboost
    • Clustering Algorithms/ Unsupervised Learning using K means
    • Deep Learning using Keras Tensor Flow (MLP ā€“Multi Layer perceptron)
    • NLP Basics
    • Tableau Basics
    • Python
    • Statistics
    • H20.ai

    Highlights of Our Data Science Course in Chennai

    • Credo Systemz is one of the most prominent Data Science training institutes that presents First Class Training in Chennai.
    • Conducted Data Science Training with well Advanced Learning Program.
    • Highly affordable Course Contents is updated by the domain Experts.
    • Well Experienced Trainers who are working in leading Top MNCs.
    • Shape your learning path with customized skills in Data Science.
    • Flexible Class Time Scheduling with tailor-made fees Structure.
    • Get Practical Knowledge with Real-time Hands-on Projects.
    • Provided Certification and Job assistance at the end of the Course completion.

    Course Features

    • Duration80 to 100 Hrs
    • Skill levelAll level
    • Batch Strength15
    • AssessmentsYes
    • Mock InterviewsYes
    • Resume BuildingYes
    • PlacementsYes
    • Flexible TimingYes
    • Fee InstallmentsYes
    • LanguageTamil/English
    CHAPTER 1: Introduction to Data Science
    • Market trend of Data Science
    • Opportunities for Data Science
    • What is the need for Data Scientists
    • What is Data science
    • Data Science Venn Diagram
    • Data Science Use cases
    • Knowing the roles of a Data Science practitioner
    • Data Science ā€“ Skills set
    • Understanding the concepts & definitions of:
      • Artificial Intelligence
      • Machine Learning- Deep Learning
      • NLP
      • Computer Vision
    CHAPTER 2: Data and Tools
    • What is Business Intelligence?
    • What is ETL?
    • Layers of a Data Warehouse
    • OLAP VS OLTP
    • Facts and Dimensions
    • Big Data tools and itā€™s uses
    • Big Data stack
    • Understanding Structured text Data
    • Understanding Unstructured text Data
    CHAPTER 3: Data Science- Deep dive
    • Understanding Descriptive vs Predictive vs Prescriptive Analytics
    • Difference between Analytics vs. Analysis
    • Data Science Project Lifecycle
    • Technology Stack Involved in the Lifecycle
      • Machine Learning tools
      • Development tools
      • Languages
      • Data Platforms
    • CRISP - Cross-industry standard process for Data Mining
    • 5WIH- The questions that kick start a ML project
    • 80-20 Rule of Data Analytics
    • Supervised Vs Unsupervised Learning
    • Data Science- Use case bubble
    • Data Mining techniques
    CHAPTER 4: Data
    • Data Wrangling or Data Munging
    • Data Categorization basics
    • Different Types of Data
    • Types of Data Collection
    • Data Sources
    • Data Collection plan
    • Data Quality Issues
    • Types of Data Error
    • Ration Scale Vs Interval Scale
    • Predictors/Features vs Predictions/Labels
    • Understanding Imbalance in Data
    CHAPTER 5: Statistics & Probability
    • What is Statistics
    • Sample Vs Population
    • Measure of Central vs Dispersion
    • Frequency Distribution
    • Cumulative Frequency Distribution
    • Mean, Median, Mode
    • Quartiles/Percentile
    • Range, Variance, Standard Deviation, Co-efficient of Variation
    • 68-95-99 Rule of SD
    • Z Score (Standard Score)
    • P-Value
    • Maximum Likelihood Estimation
    • Probability vs Likelihood
    • PDF vs PMF
    • Normal Distribution of Data
    • Skewness & itā€™s types
    • Kurtosis & itā€™s types
    • Kth Central Moments
    • Co-Variance/Joint Probability Distribution
    • Correlation
    • Entropy
    • ANOVA
    • Chi-Square
    • F tests
    • Types of Data Distribution
    Real-time Practicals
    • Hands on- Lab using pen and paper Only
    CHAPTER 6: Setup
    • Anaconda & Python
    • Understanding Jupyter Notebooks
    • Python Package Installation
    • Tableau Installation
    • Oracle Database & Server
    CHAPTER 7: Data Sourcing, Exploratory Data Analysis & Readiness
    • Concept of List, Data frame, Dictionary
    • Connecting to Databases using Python
    • Importing data from csv, text, Excel
    • Converting JSON, XML, to Data frame
    • Understanding EDA
    • Frequency Distribution
    • Analyzing NA, blanks
    • Using SQL concepts inside Python
    Real-time Practicals
    • Hands on- Lab using Python
    CHAPTER 8: Data Transformation/Wrangling
    • Handling missing Values
    • Handling Outliers
    • Normalization techniques
    • Standardization techniques
    • Regularization techniques
    • Feature Extraction
    • Train Test data selection
    Real-time Practicals
    • Hands on- Lab using Python
    CHAPTER 9: Data Science Concepts
    • No Free Lunch
    • Hypothesis vs Null Hypothesis
    • BIAS VS Variance tradeoff
    • Local Vs Global Minima/Maxima
    • Bias ā€“ Loss/ Loss-Cost Function
    CHAPTER 10: Linear Regression
    • Understanding Regression math
    • Linear Algebra concepts
    • Least Mean Square
    • Analyzing Co-relation
    • Heat Maps, Pair Plots, Distribution Graphs
    • Simple Vs Multiple Linear regression
    • Train Test data selection
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 11: Polynomial Regression
    • Understanding the math
    • Polynomial Algebra concepts
    • Degree of Polynomial
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 12: Classification
    • Overfitting/ Under fitting/ Optimal Fits
    • Handling Categorical Data inside
    • Confusion Matrix
    • Type I & Type II errors
    • Precision Vs Accuracy
    • AUC/ROC curve
    CHAPTER 13: Logistic Regression
    • Understanding the statistics behind Logistic Sigmoid
    • Logistic regression math
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 14: Random Forest
    • Understanding the Decision Tree & Bagging
    • Math behind Classification and Regression in tree
    • Decision Tree concepts
    • Using Random Forest for Regression
    • K fold Cross Validation
    • Model Optimizers
    • Hyper parameter Tuning
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 15: NaĆÆve Bayes Theorem
    • Understanding the NaĆÆve Bayes theorem
    • Bayesian Vs Gaussian theorems
    • Using naĆÆve Bayes for Regression
    • Model Optimizers
    • Hyper parameter Tuning
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 16: NLP for Machine Learning Featuring
    • Label Encoding
    • One hot encoding
    • Synonym treatment
    • Stemming
    • Lemmatization
    • Stop words
    • Parts Of Speech Tagging
    • TF-IDF and its math Behind
    Real-time Practicals
    • Hands on- Lab using Python
    CHAPTER 17: Support Vector Machine
    • Understanding the SVM Concept
    • Hyper plane and Kernel
    • Using SVM for Regression
    • Grid Search
    • Model Optimizers
    • Hyper parameter Tuning
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 18: Gradient Boosting Machine & Xgboost
    • Understanding the Boosting Concept
    • Hyper plane and Kernel
    • Learning Rate
    • Model Optimizers
    • Hyper parameter Tuning
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 19: K MEANS CLUSTERING ALGORITHM
    • Understanding Nearest Neighbors concept
    • Statistics behind K Means Clustering Algorithm
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 20: Keras Tensor flow ā€“ MLP Deep Learning (Neural Networks)
    • Understanding Deep learning
    • MLP Vs other Deep Learning
    • How Neural Network works & Architecture
    • Activation functions.
    • Model Optimizers
    • Hyper parameter Tuning
    • Best Practice and when to use DL
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 21: H20.ai
    • Introduction to H20.ai
    • Pros and Cons
    • Available models in H20.ai
    Real-time Practicals
    • Hands on- Lab & Model Implementation using Python
    CHAPTER 22: Sampling & Dimension Reduction (DR)
    • Introduction to Sampling
    • Over sampling and Under sampling
    • SMOTE/SMOTENC & Near Miss
    • Pros and Cons of sampling
    • Introduction to DR
    • PCA & itā€™s code
    CHAPTER 23: Deployment of Model to Production
    • Introduction to Pyinstaller
    • Pickle and Joblib
    Real-time Practicals
    • Hands on- Lab & Model deployment using Python
    CHAPTER 24: Tableau Basics
    • Introduction to Tableau
    • Data sources
    • Exploratory Data Analysis
    • Clustering Analysis and Inferences using Tableau
    • Creating visualizations
    Real-time Practicals
    • Hands on- Lab using Tableau

    You will be going through detailed 2 to 3 months of Data Science Hands-on training

    • Detailed instructor led sessions to help you become a proficient Expert in Data Science.
    • Build a Data Science professional portfolio by working on hands on assignments and projects.
    • Personalised mentorship from professionals working in leading companies.
    • Lifetime access to downloadable Data Science course materials, interview questions and project resources.
    Valchery pic

    Credo Systemz - Velachery, Chennai
    Call Us +91 9884412301

    Omr

    Credo Systemz - OMR, Chennai
    Call Us +91 9600112302

    Best DataScience Training Institute  
    Geetha   
    Author Picture

    This is Geetha .. I did my Data Science training in Credo Systemz. I really thank my trainer for his involvement in my Data Science training. His way of teaching is really awesome. I never saw such quality teaching and placements were so fast. Thanks a lot, Credo Systemz for your support. I like to refer credo systemz as the best Data Science training institute in Chennai.

    Data Science Training in Chennai  
    Sambath   
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    Hi, here I have attended Data Science training in Credo Systemz. I have improved my Data Science knowledge from the basic level. I really appreciate this Institute for doing such a great and better job. In every training session, I got new information about Data Science. My trainer also helped me clear interviews easily. I have referred many students in Credo for do all training.

    Job Oriented Data Science Training  
    Gowri   
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    I am Gowri and I am very glad about my Data Science training provided by Credo Systemz software training institute. Trainer explains every concept with real time examples and use cases so easy to understand every concept. My trainer way of the teaching is good. It's a best software training institute for software courses. Thank you, Credo Systemz and my trainer...

    Best Data Science Training in chennai  
    Durga Krishnan   
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    Hello everyone this is Durga from chrompet, I joined Credo Systemz for my data science training course in JAN month. I have nearly 4+ years of experience as a python developer and also having some work experience in handling data. So I like to do Data Science course, and one of my friend referred me Credo Systemz. I had attended demo session at first with the trainer, he gave a complete overview about the course which impressed me and make me to join for the course. The session went extremely well, can able to learn my real time scenarios because of the experience of the trainer. Data Science course fees also worth for this training. Now I am confident enough that I have all the skills required to be a Professional Data Scientist.

    Data science training  
    Radha   
    Author Picture

    Hai, This is Radha and recently completed my Data science training in Chennai at Credo Systemz. Iā€™m really impressed with Data science Course topics so I enrolled here. During the class, my trainer give more use cases which help us to understand the Data science concepts easily and Approaching way of a trainer in Subject is good and crystal clear, Trainer would be available through WhatsApp if we have any clarifications. Iā€™ll recommend credo systemz is the best data science training in chennai those who are looking to start data science as their career.

    Check here for candidates feedback on Data Science Training through

    Why should you choose Credo Systemz for Data Science Training in Chennai?

    If you have a dream to become an Data Scientist, then you are at the right place. We created 2000+ Data Science professionals last year. According to our Training quality and Placements, we ranked as Best Data Science training institutes in Chennai. Our Data Science expert trainers will help you to complete all the given projects, assignements and upskilling all the Data Science concepts.

    Credo Systemz is the training delivery partner for top educational institutes in Chennai. We have a separate dedicated team for Placement team and has partnered with 100+ companies. You will get frequent interview calls and all kind of supports from the placement team until you get placed.

    All our Data Science Trainers are working in top MNCs as a Data Scientists. We have top level eligibility criteria for trainers hiring. As a result you can upskilling to the latest updated Data Science concepts and learn many real-time project use cases.

    Top MNC Data Science Interview Questions

    Here you go list of Data Science Interview questions which are asked in top MNCs. We always update this section fequently with latest updated Questions and make sure our candidates have much better knowledge in these area.
    1. What Are the Feature Vectors?
    2. Explain the Steps in Making a Decision Tree.
    3. What Is Root Cause Analysis?
    4. What Is Logistic Regression?
    5. What Are Recommender Systems?
    6. Explain Cross-Validation.
    7. What Is Collaborative Filtering?
    8. Do Gradient Descent Methods at All-Time Converge to a Similar Point?
    9. What Is the Goal of A/B Testing?
    10. What Are the Drawbacks of the Linear Model?
    11. What Is the Law of Large Numbers?
    12. What Are Confounding Variables?
    13. Explain Star Schema.
    14. How Regularly Must an Algorithm be Updated?
    15. What Are Eigenvalue and Eigenvector?
    16. Why Is Resampling Done?
    17. Explain Selective Bias.
    18. What Are the Types of Biases That Can Occur During Sampling?
    19. Explain Survivorship Bias.
    20. How Do You Work Towards a Random Forest?
    1. Estimate the probability of a disease in a particular city given that the probability of the disease on a national level is low.
    2. How will inspect missing data and when are they important for your analysis?
    3. How will you decide whether a customer will buy a product today or not given the income of the customer, location where the customer lives, profession and gender? Define a machine learning algorithm for this.
    4. From a long sorted list and a short 4 element sorted list, which algorithm will you use to search the long sorted list for 4 elements.
    5. How can you compare a neural network that has one layer, one input and output to a logistic regression model?
    6. How do you treat colinearity?
    7. How will you deal with unbalanced data where the ratio of negative and positive is huge?
    8. What is the difference between -
      1. Stack and Queue
      2. Linkedin and Array
    1. Will Uber cause city congestion?
    2. What are the metrics you will use to track if Uberā€™s paid advertising strategies to acquire customers work? How will you figure out the acceptable cost of customer acquisition?
    3. Explain principal components analysis with equations.
    4. Explain about the various time series forecasting technqiues.
    5. Which machine learning algorithm will you use to solve a Uber driver accepting Ā request?
    6. How will you compare the results of various machine learning algorithms?
    7. How to solve multi-collinearity?
    8. How will you design the heatmap for Uber drivers to provide recommendation on where to wait for passengers? How would you approach this?
    9. If we added one rider to the current SF market, how would that affect the existing riders and drivers?
    10. What are the different performance metrics for evaluating Uber services?
    11. How will you decide which version (Version 1 or Version 2) of the Surge Pricing Algorithms is working better for Uber ?
    12. How will you explain JOIN function in SQL to a 10 year old ?
    1. Write the code to reverse a Linked list.
    2. What assumptions does linear regression machine learning algorithm make?
    3. A stranger uses a search engine to find something and you do not know anything about the person. How will you design an algorithm to determine what the stranger is looking for just after he/she types few characters in the search box?
    4. How will you fix multi-colinearity in a regression model?
    5. What data structures are available in the Pandas package in Python programming language?
    6. State some use cases where Hadoop MapReduce works well and where it does not.
    7. What is the difference between an iterator, generator and list comprehension in Python?
    8. What is the difference between a bagged model and a boosted model?
    9. What do you understand by parametric and non-parametric methods? Explain with examples.
    10. Have you used sampling? What are the various types of sampling have you worked with?
    11. Explain about cross entropy ?
    12. What are the assuptions you make for linear regression ?
    13. Differentiate between gradient boosting and random forest.
    14. What is the signigicance of log odds ?
    1. Explain what regularization is and why it is useful.
    2. Which data scientists do you admire most? which startups?
    3. How would you validate a model you created to generate a predictive model of a quantitative outcome variable using multiple regression.
    4. Explain what precision and recall are. How do they relate to the ROC curve?
    5. How can you prove that one improvement you've brought to an algorithm is really an improvement over not doing anything?
    6. What is root cause analysis?
    7. Are you familiar with price optimization, price elasticity, inventory management, competitive intelligence? Give examples.
    8. What is statistical power?
    9. Explain what resampling methods are and why they are useful. Also explain their limitations.
    10. What is selection bias, why is it important and how can you avoid it?
    11. What do you mean by word Data Science?
    12. Explain the term botnet?
    13. What is Data Visualization?
    14. Why data cleaning plays a vital role in analysis?
    15. What is Linear Regression?
    16. What do you understand by term hash table collisions?
    17. Compare and contrast R and SAS?
    18. What do you understand by letter ā€˜Rā€™?
    19. What is the goal of A/B Testing?
    20. What is an Eigenvalue and Eigenvector?
    1. Derive the equations for GMM.
    2. What do you mean by word Data Science?
    3. Explain the term botnet?
    4. What is Data Visualization?
    5. How you can define Data cleaning as a critical part of process?
    6. Point out 7 Ways how Data Scientists use Statistics?
    7. Differentiate between Data modeling and Database design?
    8. Describe in brief the data Science Process flowchart?
    9. What are Recommender Systems?
    10. Why data cleaning plays a vital role in analysis?
    11. What is Linear Regression?
    12. What do you understand by term hash table collisions?
    13. Compare and contrast R and SAS?
    14. What do you understand by letter ā€˜Rā€™?
    15. What is Interpolation and Extrapolation?
    16. What is Collaborative filtering?
    17. How to design a customer satisfaction survey?
    18. Explain a probability distribution that is not normal and how to apply that?
    19. Describe the process of data analysis?
    20. Mention what is data cleansing?
    1. What do you mean by word Data Science?
    2. Explain the term botnet?
    3. What is Data Visualization?
    4. How you can define Data cleaning as a critical part of process?
    5. Differentiate between Data modelling and Database design?
    6. Differentiate between Data modelling and Database design?
    7. What are Recommender Systems?
    8. Why data cleaning plays a vital role in analysis?
    9. What is Linear Regression?
    10. What do you understand by term hash table collisions?
    11. Compare and contrast R and SAS?
    12. What do you understand by letter ā€˜Rā€™?
    13. What is Interpolation and Extrapolation?
    14. What is the difference between Cluster and Systematic Sampling?
    15. Are expected value and mean value different?
    16. What does P-value signify about the statistical data?
    17. What is the goal of A/B Testing?
    18. What is an Eigenvalue and Eigenvector?
    19. How can you assess a good logistic model?
    20. What is Machine Learning?
    1. Python or R ā€“ Which one would you prefer for text analytics?
    2. Differentiate between univariate, bivariate and multivariate analysis.
    3. Define some key performance indicators for the product
    4. Which technique is used to predict categorical responses
    5. What is logistic regression? Or State an example when you have used logistic regression recently.
    6. What are Recommender Systems?
    7. Why data cleaning plays a vital role in analysis?
    8. Differentiate between univariate, bivariate and multivariate analysis.
    9. What do you understand by the term Normal Distribution?
    10. What is Linear Regression?
    11. What is Interpolation and Extrapolation?
    12. What is power analysis?
    13. What is the difference between Cluster and Systematic Sampling?
    14. Are expected value and mean value different?
    15. What does P-value signify about the statistical data?
    16. How you can make data normal using Box-Cox transformation?
    17. What is the goal of A/B Testing?
    18. What is an Eigenvalue and Eigenvector?
    19. What is Gradient Descent?
    1. How can you assess a good logistic model?
    2. What are various steps involved in an analytics project?
    3. During analysis, how do you treat missing values?
    4. Explain about the box cox transformation in regression models
    5. Can you use machine learning for time series analysis?
    6. Write a function that takes in two sorted lists and outputs a sorted list that is their union.
    7. What is Regularization and what kind of problems does regularization solve?
    8. What is multicollinearity and how you can overcome it?
    9. What is the curse of dimensionality?
    10. How do you decide whether your linear regression model fits the data?
    11. What is the difference between squared error and absolute error?
    12. What is Machine Learning?
    13. How are confidence intervals constructed and how will you interpret them?
    14. How will you explain logistic regression to an economist, physican scientist and biologist?
    15. How can you overcome Overfitting?
    16. Differentiate between wide and tall data formats?
    17. Is NaĆÆve Bayes bad? If yes, under what aspects.
    18. How would you develop a model to identify plagiarism?
    19. How will you define the number of clusters in a clustering algorithm?
    20. Is it possible to perform logistic regression with Microsoft Excel?
    1. Can you enumerate the various differences between Supervised and Unsupervised Learning?
    2. What do you understand by the Selection Bias? What are its various types?
    3. Please explain the goal of A/B Testing.
    4. How will you calculate the Sensitivity of machine learning models?
    5. Could you draw a comparison between overfitting and underfitting?
    6. Between Python and R, which one would you pick for text analytics and why?
    7. Please explain the role of data cleaning in data analysis.
    8. What do you mean by cluster sampling and systematic sampling?
    9. Please explain Eigenvectors and Eigenvalues.
    10. Can you compare the validation set with the test set?
    11. What do you understand by linear regression and logistic regression?
    12. Please explain Recommender Systems along with an application.
    13. Could you explain how to define the number of clusters in a clustering algorithm?
    14. What do you understand by Deep Learning?
    15. How does Backpropagation work? Also, it state its various variants.
    16. What do you know about Autoencoders?
    1. What do you know about Autoencoders?
    2. Please explain the concept of a Boltzmann Machine.
    3. What do you understand by linear regression?
    4. What do you understand by logistic regression?
    5. What is a confusion matrix?
    6. What is the difference between supervised and unsupervised machine learning?
    7. What is bias, variance trade off ?
    8. What is exploding gradients ?
    9. What is a confusion matrix ?
    10. Explain how a ROC curve works ?
    11. What is selection Bias ?
    12. Explain Decision Tree algorithm in detail.
    13. What is Ensemble Learning ?
    14. What is a Box Cox Transformation?
    15. What is deep learning?
    16. What are Recommender Systems?
    17. What is the difference between Regression and classification ML techniques?
    1. What is Data Science?
    2. What is logistic regression in Data Science?
    3. Name three types of biases that can occur during sampling
    4. Discuss Decision Tree algorithm
    5. What is Prior probability and likelihood?
    6. Explain Recommender Systems?
    7. Name three disadvantages of using a linear model
    8. Why do you need to perform resampling?
    9. List out the libraries in Python used for Data Analysis and Scientific Computations.
    10. What is Power Analysis?
    11. Explain Collaborative filtering
    12. What is bias?
    13. Discuss 'Naive' in a Naive Bayes algorithm?
    14. What is a Linear Regression?
    15. State the difference between the expected value and mean value
    16. What the aim of conducting A/B Testing?
    17. What is Ensemble Learning?
    18. Explain Eigenvalue and Eigenvector
    1. Explain Eigenvalue and Eigenvector
    2. Define the term cross-validation
    3. Explain the steps for a Data analytics project
    4. Discuss Artificial Neural Networks
    5. What is Back Propagation?
    6. What is a Random Forest?
    7. What is the importance of having a selection bias?
    8. What is the K-means clustering method
    9. Explain the difference between Data Science and Data Analytics
    10. Explain p-value?
    11. Define the term deep learning
    12. Explain the method to collect and analyze data to use social media to predict the weather condition.
    13. When do you need to update the algorithm in Data science?
    14. What is Normal Distribution
    15. Which language is best for text analytics? R or Python?
    16. Explain the benefits of using statistics by Data Scientists
    17. Name various types of Deep Learning Frameworks
    18. What is skewed Distribution & uniform distribution?
    19. What is reinforcement learning?
    20. What is precision?
    1. What is skewed Distribution & uniform distribution?
    2. When underfitting occurs in a static model?
    3. What is reinforcement learning?
    4. Name commonly used algorithms.
    5. What is precision?
    6. What is a univariate analysis?
    7. How do you overcome challenges to your findings?
    8. Explain cluster sampling technique in Data science
    9. State the difference between a Validation Set and a Test Set
    10. Explain the term Binomial Probability Formula?
    11. What is a recall?
    12. Discuss normal distribution
    13. While working on a data set, how can you select important variables? Explain
    14. Is it possible to capture the correlation between continuous and categorical variable?
    15. Discuss Artificial Neural Networks
    16. What is Back Propagation?
    17. What is a Random Forest?
    18. Explain Recommender Systems?
    19. Explain Collaborative filtering
    1. How do data scientists use statistics?
    2. Whatā€™s the difference between SAS, R, And Python Programming?
    3. What are interpolation and extrapolation?
    4. What is the difference between population and sample in data?
    5. What are the steps in making a decision tree?
    6. How is machine learning deployed in real-world scenarios?
    7. How is machine learning deployed in real-world scenarios?
    8. What do you mean by the term linear regression?
    9. What is the difference between extrapolation and interpolation?
    10. What is the purpose of A/B testing?
    11. How different is a mean value different from expected value?
    12. Why is it mandatory to clean a data set?
    13. What are the steps involved in analytics projects?
    14. What do you understand by the term recommender systems?
    15. If you had to choose between the programming languages R and Python, Which one would you use for text analytics?
    16. For linear regression, what are some of the assumptions a data scientist is most likely to make?
    17. How do you find the correlation between a categorical variable and a continuous variable?
    Get Answer for all the above questions and place in your dream company

    Learning Outcomes of our Best Data Science Course in Chennai

    • Develop and Get Upgradable skills in programming abilities like loop functions and debugging tools.
    • Proficiency skills in a broad range of methods based statistics and informatics using Data Management and problem-solving.
    • Understanding and ability to solve real-world problems using data mining software.
    • Recognize and analyze the principles of Data Science.
    • Enhances the skills in high complex tools and algorithms of Data Science.
    • Get Professional Knowledge in the importance of Python and BigData technologies.
    • Develop the Programmatical skill efficiency in R.
    • Mastering in Programming techniques and knowledge representation.

    Data Science Upcoming Batch Details

    Credo Systemz ranked as Best Data Science Training Institute in Chennai. Offering Data Science Classroom training and as well Data Science Online Training. Please check our upcoming Data Science course in Chennai Velachery, OMR start dates and Online.

    13
    Dec
    Data Science Online Training
    12:00 am - 12:00 am
    online
    datepic

    Canā€™t find a batch you were looking for?

    Data Science Interview Questions and Answers

    Here are the top 100 Data Science Interview questions and Answers which is prepared by our Data Scientists. The Answers are having both coding, statistics and algorithms in easy manner. The 100 Interview Questions and Answers will be used to recall whatever you learnt in our classroom training as practical.

    Data Science Combo Courses

    Data Science is also called an interdisciplinary field which uses different technologies to get useful insight from the data. Hence as a Data Scientist it is very much needed to learn about different technologies related to the Data Science field, to match that we have listed down best combo packages with our Data Science course to gain the required skill set to be an successful Data Scientist.

    Top Factors which makes us the Best Data Science Training Institute in Chennai

    • Offering Data Science Training in Chennai on both weekdays, weekend and also Online training.
    • Flexible session timings and comfort environment, So that you can choose us to learn Best Data Science Training in Chennai.
    • Learn Data Science from More than 10+ years working professional, as a result, you become a Data Scientist.
    • During our Data Science course, you will analysis real time live data and submit your report.
    • Additionally the BEST Software Training Institute for Best Data Science Course.
    • Professional Data Science Training in Chennai from the Data Scientists same as you become a Data Scientist.
    • Best Data Science Training in Chennai with Real Time ground data rather than theory Oriented.
    • Ranked as BEST Data Science Training Institute in Chennai Velachery based on all positive reviews across the Internet.
    • Credo Systemz is the Best Data Science Training center in Chennai.
    • Furthermore Our Data Science Course syllabus is standard and unique with “R” Programming.
    • Offering Data Science Course in Chennai with real-time data. Finally, you reached Best Data Science Training Institute in Chennai.

    FAQ

    Data Science is a mandatory one in almost all industries. It includes statistics, maths, programming, analytics, databases, etc., Anyone can learn Data Science and implement it in their organization or business.
    Those who love Algorithms, coding, algebra, analytics, etc.,
    • Problem Solver
    • Students who having mathematics and statistics as their specialized subjects in their graduation or post-graduation.
    • Those who interestedĀ in Big Data and Machine Learning.
    • Wants to improve their business strategy according to the market standard.
    • Who loves to create visual data.
    • Interested in learning what are the new technologies behind all the real-time innovative changes.
    • Very much interest in future technologies and Artificial Intelligence.
    Credo Systemz is one of the Best Data Science Training Institute in Chennai Velachery and OMR. We are following a unique Data Science program structure with the below features,
    • 90+ hours of live interactive sessions.
    • 30+ real-time use cases.
    • Sessions handled by Industry Experts.
    • Unique course content.
    • Well structured Course Materials.
    • 10+ real-time projects.
    • Flexible batch timings.
    • End to end placement assistance.
    • Free workshops with the latest updation in Data Science.
    • Options to discuss with our Alumni and get knowledge from them.
    • Lifetime support with any technical helps.
    • Installment payment options.
    You become a Master with the below skill sets with our Best Data Science course in Chennai.
    • Machine Learning
    • Programming
    • Linear Algebra and Calculus
    • Data Wrangling
    • Statistics
    • Data Intuition
    • Data Visualization
    • Communication
    • Software Engineering
    Definitely you can!
    Everyone has some goals and dreams!! You have to choose the right place to achieve the goal and fulfill your dream. To achieve the goal we need the right guidance, Credo Systemz is the one who helps the people to make their dream comes real.

    You can attend a full live classroom session and interact with our trainer. You can clarify all our doubts without paying anything.
    • You become a great problem solver.
    • Expert in handling huge volumes of structured and unstructured data.
    • High analytical skills and deep knowledge in Machine learning.
    • Become a professional in Data processing and Data modeling.
    • Good in Algorithms to generate the right Data visualization.
    • Great predictions with effective reports.
    • Become an expert in Big Data platforms.
    • Familiar with Cloud tools.
    • Sufficient Software Engineering skills.
    Of course!
    We are offering Data Science course in-classroom training and also online training. We have 2 types of batches as Weekdays and Weekends. You can change your batches anytime without paying any extra cost.
    Definitely!
    You can attend your missed topics with any batches. Not only missed, if you are not clear with any topics as well you can attend the same topics with some other batch.
    Our motto is sharing highly standard Data Science knowledge to our candidates and making them as a successful Data Scientist.
    No hurries!!
    One of the main reasons for our Alumni referring us is we are not money minded. You can pay your course fee as installments.
    Our Data Science instructor having 15+ years of experience in IT industry. They are currently working as Data Scientists in top MNCs.
    Credo Systemz following a huge interview process and set of rules to hire a mentor for any courses. As a result, we have highly professions industry experts as trainers.
    Yes!
    At the end of the course, we will provide you a course completion certificate which is accepted by almost all the companies.
    Also, we help our candidates to do the official certifications since we are an authorized Pearson VUE certification exam center.
    100%
    From the start, we will monitor your performance and update the feedbacks. We have a separate placement team and they will follow the below process,
    • Resume Preparation
    • Periodic Mock Interviews to test your subject knowledge.
    • Connecting you with our Alumni to get their experience and job openings in their organizations.
    • We have 50+ clients and providing job offers with them.
    • Offering real-time projects to clear any machine tests.
    • Providing 300+ Interview Questions and Answers.

    Related Trainings

    Nearby Access Areas
    Our Velachery and OMR branches are very nearby access to the below locations.
    Medavakkam, Adyar, Tambaram, Adambakkam, OMR, Anna Salai, Velachery, Ambattur, Ekkattuthangal, Ashok Nagar, Poonamallee, Aminjikarai, Perambur, Anna Nagar, Kodambakkam, Besant Nagar, Purasaiwakkam, Chromepet, Teynampet, Choolaimedu, Madipakkam, Guindy, Navalur, Egmore, Triplicane, K.K. Nagar, Nandanam, Koyambedu, Valasaravakkam, Kilpauk, T.Nagar, Meenambakkam, Thiruvanmiyur, Nungambakkam, Thoraipakkam, Nanganallur, St.Thomas Mount, Mylapore, Pallikaranai, Pallavaram, Porur, Saidapet, Virugambakkam, Siruseri, Perungudi, Vadapalani, Villivakkam, West Mambalam, Sholinganallur.
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