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Data Science Course in Mumbai | Data Science Course in Pune

Introduction to Data Science Courses

Data Science all together can be said as a perfect blend of mathematics, Business acumen, tools machine techniques, etc. This combination helps us to find out the hidden insight from raw data. This is very widely used in making big business decisions.

Data science deals with both structured and unstructured Data. It also takes the use of predictive analysis to get the result. Therefore data science is all about the past, present, and future. For example, an immediate decision is based on past historical data, which give a better picture of future requirement. On that basis, the present decision can be taken. And also, current data helps make the next decision as well.

Data science Training

Importance of Learning Data Science

With the revolution of time and an extreme level of competition, the companies must analyze the environment before making any decision. Data science has become the necessity of companies. Companies usually involve various departments working for it. For example, the finance department, it departs, the HR team, and many more. To make out the data from all of this depart, they require the data scientist. The data scientist is the only one who could help these companies to get it collected and arranged wisely. Therefore with the growing market, the requirement of data scientists has increased.

Companies are also offering a handsome salary to the data scientist. As per the fact, IBM has declared in the 21st century, and data scientist is one of the most trending jobs. This is the field where the person, if interested, can make their career in the data science field.

Learn the concepts of data science from industry experts and give a pace to your career. We have a team of qualified trainers and professionals who train our students with their knowledge and experience.

Significant Components of Data Science Course

The data science course mainly consists of three major components. Firstly machine learning, secondly Big DATA, and the last one is business intelligence. Let discuss in brief on components of data science and significance of Data Science Course in Mumbai.

Machine Learning

Machine learning involves learning and adapting the advancement. It mainly includes algorithms and mathematical models. For example, time-series forecasting is in a trend where the machine predicts the outcome of the coming month or years.

Big Data

With the increase in population and growing market, population is also increasing with time . Every click, every like, and every view of the video when summed up together, it is said to be big data. Big data is available in a significant amount, which is un-structured. With the help of this vital data component, the unstructured data is converted into structured data, and make it easy to analyze.

Business Intelligence

Every business is producing even more day by day. Therefore it is essential to analyze the past data to be very sure to take the future decision, so it does not hamper future growth.

Benefits of Choosing Data Science course as a Career

Data science has become a hot career topic among employees. This is because of the available career options in data science. But in spite of being very common, there are only a few people who have in-depth knowledge and understanding of data science. But the same could be gained by attending Data Science training in Mumbai. Although all the advantages cannot be listed here, still some of the significant benefits are mentioned to help you opt for data science as your career.

High Demand

With the growing market and an increase in data, the demand for data scientists is increasing day by day. As per LinkedIn, data science will give the job to about 11.5 million people by 2026. Therefore one opting for data Science as a career will never be left unemployed.

Highly Paid Job

As compared to some other career option data science job is one of the highly paid jobs. With the increase in demand and low availability of data scientists, the companies are ready to pay more as compared to other employees. Not only pay but the scientist for data science also acquire a good position in the company. They are counted as high-level officers of the companies

Summary

With information mentioned above, we can get a wide idea about data science. Opting for our data science course in Pune as a career is a good option in a professional way as well as in personal style. The data science will not only give you better career option but will also make you a better person. As it will develop decision making quality and will also increase problem-solving quality in you.

Who should do the Data Scientist Course?

The demand for professional data scientists is increasing day by day which has made data science certification course best suited for experienced people. This course is recommended for:

  • IT professionals planning to switch in trending skills of data science like analytics, machine learning and Artificial intelligence.
  • Domain Expertise who want improve their Business decision making process based on data insights.
  • Software developers planning to switch their options to data science and data analytics or machine learning
  • Any Graduates planning to build a career in the data science field.
  • Experienced professionals willing to pursue data science
  • People with a genuine interest in the field of Machine Learning and Artificial Intelligence

What we offers You In Training ?

The perquisites of learning data science are:

  • Classroom training: Theory classes supplemented with Practical's. All the presentations used during the classroom teaching & other material.
  • Set of hands-on exercises with detailed step-by-step procedures to solve them.
  • We help our student to participate in international data science competition.
  • All software that are needed to work-out the Hands-on exercises. Interview questions that are recently & frequently asked by the employers.
  • Customized Learning plan, based on your background Certificate of Course completion & Star performance
  • If you have any queries even after completion of the course, you can call/mail/meet us directly, we will be there to help you out

Cirriculam

PART A – Python for Data Science

  • Module 1 (Python Programming Basics)

    Learning Objectives - In this, you will understand basic of python.

    • Installing Jupyter Notebooks
    • Python Overview
    • Python Identifiers
    • Various Operators and Operators Precedence
    • Getting input from User,Comments,Multi line
  • Module 2 (Working With Python Data Types)

    Learning Objectives - In this module, you will understand to work with different Python data types.

    Topics-

    • Mutable and immutable Data Types
    • Python Lists, Tuples, Dictionaries and Sets
    • Accessing Values
    • Basic Operations
    • Indexing, Slicing, and Matrixes Built-in Functions & Methods
    • Exercises on List, Tuples And Dictionary
  • Module 3 (Conditional and Loop Statements)

    Learning Objectives - In this module, you will understand different Conditional and Loop statements in Python.

    Topics

    • Understanding and using Simple if Statement,
    • Understanding and using if-else Statement
    • Understanding and using if-elif Statement.
    • Introduction To while Loops.
    • Introduction To for Loops
    • Using continue and break
  • Module 4 (Functional Programming in Python)

    Learning Objectives - In this module, you will learn about how to use and create functions in python

    Topics

    • Introduction To Functions – Why Defining Functions
    • Creating user Define functions
    • Calling Functions
    • Anonymous Functions - Lambda expression
  • Module 5 (Read-Write Operation in Python )

    Learning Objectives - In this module, you will learn read and write the files in python

    Topics

    • How to Create a Text File
    • How to Append Data to a File
    • How to Read a File
    • How to Read a File line by line
    • File Modes in Python
    • How to write the file
  • Module 6 (Data Science specific Library - Numpy)

    Learning Objectives - In this module, you will learn about the various functions and operation of NumPy .

    Topic-

    • What is Numpy and how to install and use Numpy
    • Numpy v/s Lists
    • Creating Arrays from Python Lists
    • Creating Arrays from Scratch
    • NumPy Standard Data Types
    • NumPy Array Attributes
    • Array Indexing: Accessing Single Elements
    • Array Slicing: Accessing Subarrays
    • Reshaping of Arrays
    • Array Concatenation and Splitting
    • Summing the Values in an Array
    • Minimum and Maximum in Array
    • Array Sorting
  • Module 7 (Data Science specific Library - Pandas)

    Learning Objectives - In this module, you will learn about the various functions and operation of Pandas

    Topics-

    • What is Pandas and how to install and use Pandas
    • Introducing Pandas Objects
    • Pandas Series Object
    • Pandas DataFrame Object
    • Data Indexing and Selection in Series and Dataframe
    • Pandas – Missing Data
    • Pandas – Aggregations
    • Pandas – GroupBy
    • Pandas – Merging/Joining
    • Pandas – Concatenation
    • Pandas – Sorting
    • Pandas – Date Functionality
    • Pandas – Timedelta

  • Module 8 (Data Science specific Library - Matplotlib)

    Learning Objectives - In this module, you will learn about the various functions and operation of Matplotlib which are for visualization.

    Topics

    • What is matplotlib and how to install and use it
    • Introducing Matplotlib pyplot
    • Matplotlib – axes class
    • Matplotlib – Formating the axes
    • Matplotlib – Multiplot
    • Matplotlib – Subplot
    • Matplotlib – Bar Plot
    • Matplotlib – Histrogram
    • Matplotlib – Scatter plot
    • Matplotlib – Boxplot
  • Module 9 (Data Science specific Library - Sklearn)

    Learning Objectives - In this module, you will learn about the various functions and operation of Scikit-learn which are for Machine learning.

    Topics-

    • Machine learning Overview
    • Introduction to Scikit-learn
    • Installation of Scikit-learn
    • Scikit-learn - Preprocessing
    • Scikit-learn - Regression , Classification and Model Selection
    • Various case studies to understand and implement machine learning algo through Scikit-learn

PART B – R programming for Data Science

  • Module 1 (R Basics and Background)

    Learning Objectives - In this, you will understand basic of R.

    Topics

    • Introduction to R and Why R
    • Installing R and Rstudio
    • Installing Packages – Loading And Unloading Packages
    • Arithmetic, Relational, Logical and Assignment Operators
  • Module 2 (Working With R Data Types)

    Learning Objectives - In this module, you will understand to work with different R data types.

    Topics

    • Understand different data types in R
    • Creating and manipulating Vectors
    • Creating , Indexing, Slicing - Matrix
    • Creating , Indexing, Slicing - Array
    • Creating , Indexing, Slicing - List
    • Creating , Indexing, Slicing/Subsetting - Dataframe
    • Factors
    • Exercises on List, vectors, Factors and Dataframe
    • Data accessing and manuplication by dplyr package
  • Module 3 (Conditional and Loop Statements)

    Learning Objectives - In this module, you will understand different Conditional and Loop statements in R.

    Topics

    • Understanding and using Simple if Statement,
    • Understanding and using if-else Statement
    • Understanding and using ifelse Statement.
    • Introduction to while Loops.
    • Introduction to for Loops
    • Using break
  • Module 4 (Functional Programming in R)

    Learning Objectives - In this module, you will learn about how to use and create functions in python

    Topics

    • Introduction To Functions – Why Defining Functions
    • Creating user Define functions
    • Calling Functions
    • Important In-built Functions
  • Module 5 (Read-Write Operation in R)

    Learning Objectives - In this module, you will learn read and write the files in python

    Topics

    • How to import and Export text files in R
    • How to import and Export csv files in R
    • How to import and Export xlxs files in R
  • Module 6 (Visualization in R)

    Learning Objectives - In this module, you will learn about the various functions and operation of Matplotlib which are for visualization.

    Topics

    • Creating Bar Plot
    • Creating Histrogram
    • Creating Scatter plot
    • Creating Boxplot
    • Creating Pie plot
    • Adding Legends and labels to plots
    • Formatting the plot using par parameters
    • Create all the above plot for more effective Visualization in R using ggplot2

PART C – Statistics

  • Module 1 (Descriptive Statistics)

    Learning Objectives - In this, you will understand descriptive Statistics.

    Topics

    • Understanding Types of Data
    • Types of Descriptive Statistics?
    • Measure of Central Tendency – Mean, Median, Mode
    • Measure of Spread / Dispersion – Range, Variance, standard deviation
    • Skewness
    • Kurtosis
    • Correlation
  • Module 2 (Probability and statistics )

    Learning Objectives - In this, you will understand descriptive Statistics.

    Topics

    • Probability and Central Limit Theorem
    • Exploratory Data Analysis
    • Normal Distribution
    • Understanding the sampling and it importance
    • Different type of Sampling Method
    • Importance of Hypothesis Testing in Business
    • Basics of Hypothesis Testing
    • Null and Alternate Hypothesis
    • Understanding Types of Errors – Type I and Type II erroes
    • Confidence Interval and Confidence level
    • Z-Test
    • P Value
    • Degree of Freedom

PART D – Machine Learning and AI Using R & Python

  • Module 1 (Introduction to Machine Learning and AI)

    Learning Objectives - In this, you will understand basic of ML and AI.

    Topics

    • What is Machine Learning?
    • What is Artificial Intelligence?
    • Application of Machine Learning and AI
    • Introduction to Supervised Learning, Unsupervised Learning
  • Module 2 (Linear Regression)

    Learning Objectives - In this module, you will understand working and practical application of linear regression.

    Topics

    • Introduction to Linear Regression
    • Linear Regression with Multiple Variables
    • Assumptions of Linear Regression
    • Disadvantage of Linear Models
    • Case Study on Linear regression Model thru Python
    • Case Study on Linear regression Model thru R
    • Interpretation of Model Outputs
    • Evaluation Metric for Linear regression – Mean absolute error, MAPE etc
  • Module 3 (Logistic Regression)

    Learning Objectives - In this module, you will understand working and practical application of logistic regression

    Topics

    • Introduction to Logistic Regression
    • Why Logistic Regression
    • Odds Ratio
    • Advantages and Disadvantages of Logistic Regression
    • Case Study on Logistic regression Model thru Python
    • Case Study on Logistic regression Model thru R
    • Evaluation Metric for Logistic regression - Confusion Matrix, ROC and AUC
  • Module 4 (Classification Models)

    Learning Objectives - In this module, you will understand working and practical application of different classification models

    Topics

    • Decision Tree
      • ◦ Introduction to Decision Tree
      • ◦ How does the Decision Tree algorithm work?
      • ◦ Attribute Selection Measures – Gini Index, information gain
      • ◦ Case Study on Decision Tree thru Python
      • ◦ Case Study on Decision Tree thru R
      • ◦ Visualize the Decision Tree
      • ◦ What is Over fitting
      • ◦ What is Pruning in decision tree and types of pruning
    • Random Forest
      • ◦ What are different types of Ensemble learning techniques
      • ◦ Introduction to Random Forest
      • ◦ How does the Random Forest algorithm work?
      • ◦ How to select optimal number of trees in Random Forest
      • ◦ Case Study on Random Forest thru Python
      • ◦ Case Study on Random Forest thru R
    • Support Vector Machine
      • ◦ Introduction to SVM
      • ◦ How does the SVM work?
      • ◦ Case Study on SVM thru Python
      • ◦ Case Study on SVM thru R
  • Module 5 (Unsupervised Machine Learning )

    Learning Objectives - In this module, you will understand working and practical application of different Unsupervised Machine learning technique

    Topics

    • What is Clustering
    • What are the clustering types
    • What is Kmeans
    • How Kmeans clustering works
    • How to find the optimal number of clusters
    • Case Study on Kmeans clustering thru Python
    • Case Study on Kmeans clustering thru R
    • What is PCA?
    • Understanding the PCA
    • Case Study on PCA thru Python
    • Case Study on PCA thru R
    • What is Association Rule Mining?
    • Association Rule Strength Measures – Support, confidence and lift
    • Case Study on PCA thru Python
    • Case Study on PCA thru R
  • Module 6 (Natural Language Processing)

    Learning Objectives - In this module, you will understand basic working and practical application of NLP.

    Topics

    • Introduction to natural Language Processing(NLP)
    • What are different NLP components
    • Understand the text mining and document term matrix
    • Case Study on Text mining
    • Twitter data analysis thru NLP
  • Module 7 (AI – Deep learning-Neural Network)

    Learning Objectives - In this module, you will understand basic working and practical application of NLP.

    Topics

    • Understanding Neural Networks
    • From Biological to Artificial Neurons
    • Different types of Neural Network
    • Activation Functions
    • The Number of Layers
    • How does the neural Network Learn
    • What is Gradient descent
    • What is Back propagation
    • Case Study on Neural Network thru Python
    • Case Study on Neural Network thru R

PART E – Tableau For Data Visualization

  • Module 1 (Introduction to Tableau)

    Learning Objectives - In this, you will understand basic Tableau.

    Topics

    • • Introduction to Tableau?
    • • Tableau desktop Installation
    • • Why Tableau is important?
    • • Tableau interface
  • Module 2 (Tableau – Data Visualization)

    Learning Objectives - In this, you will understand more about Tableau functionality and how to perform them in tableau.

    Topics

    • • Connecting Data
    • • Joins
    • • Data Types
    • • Dimensions & Measures
    • • Show Me
    • • Filters
    • • Groups
    • • Sets
    • • Building Charts and Graphs in Tableau
    • • Aggregate Functions
    • • Date Functions
    • • Calculated Fields
    • • Creating dashboard pages
    • • Case Study For Visualization

PART F – Projects & Assignments

  • Project 1
    Project Title:
    Evaluating the performance of mutual funds.
    Industry:
    Finance
    Objective:
    To determine which mutual category is best for investment based on the 5 years historical data of low risk , moderate risk and high risk funds .
    Project Task:
    1. calculate annualized average returns separately for Low risk , medium risk and High risk funds.?
    2. 2) calculate variability present in the returns ?
    3. 3) calculate and compare the returns variability amongst all three mutual fund category for e.g. Are the returns for high risk
    4. funds more variable than low or medium risk funds ?
    Tools:
    Use R and Python Tool
    Techniques:
    Use Measures of central tendency (Hint : Mean Mode , Median ,CV ,Standard Deviation )
  • Project 2
    Project Title
    To study the intension of households to purchase a big screen television in next 12 month .
    Industry
    Electronics
    Project Task:
    1. What is the probability that household is planning to purchase a big screen television in the next year?
    2. What is the probability that household will actually purchase big screen television?
    3. What is the probability that household is planning to purchase a big screen television and purchase big screen television?
    4. What is the probability that household that purchases a big screen television will also purchase DVD Player .
    5. What is the probability that a household that purchases a big screen television will be satisfied with purchase?
    Tools:
    Use R and Python Tool
    Techniques:
    Use Basics and Conditional probability.
  • Project 3
    Project Title:
    Predicting sales for clothing store
    Industry:
    Retail and Garments
    Objective:
    To determine the sales of clothing store based on the size of store .
    Project Task:
    1. Need to forecast the annual sales for store based on size of stores of various location this would help franchise to open new stores .
    2. Use linear regression method that can be used to predict the values of sales (dependent variable) based on area of stores (independent variable )
    Tools:
    Use R and Python Tool
    Techniques:
    Use Linear Regression method.
  • Project 4
    Project Title:
    Identifying Good and Bad Customers for Granting Loans
    Industry:
    Banking
    Objective:
    To eradicate the risk of defaulters on given Loan before offering credit .
    Project Task:
    1. Calculate the credit score of the customers based on demographics data of customer
    2. Calculate the predicated probabilities for customers (chances) Being default on loan or not being Default on loan
    Tools:
    Use R and Python Tool
    Techniques:
    Use Logistic Regression method .
  • Project 5
    Project Title:
    predict the sales price for each house. For each Id in the test set, you must predict the value of the SalePrice variable.
    Industry:
    Housing and real estate
    Tools:
    Use R and Python Tool
    Techniques:
    Advanced regression techniques like random forest and gradient boosting
  • Project 6
    Project Title:
    forecasting revenue for three different companies
    Industry:
    FMCG
    Objective:
    To Forecast seasonal sales for three different company
    Project Task:
    Need to forecast revenues for three companies in order to better evaluate investment opportunities to your clients
    Tools:
    Use R and Python Tool
    Techniques:
    Use Arima time series forecasting method .

PART G – Data Science Interview Questions

  • What are the differences between overfitting and underfitting?

    In statistics and machine learning, one of the most common tasks is to fit a model to a set of training data, so as to be able to make reliable predictions on general untrained data.

    In overfitting, a statistical model describes random error or noise instead of the underlying relationship. Overfitting occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model that has been overfit has poor predictive performance, as it overreacts to minor fluctuations in the training data.

    Underfitting occurs when a statistical model or machine learning algorithm cannot capture the underlying trend of the data. Underfitting would occur, for example, when fitting a linear model to non-linear data. Such a model too would have poor predictive performance.

  • Python or R – Which one would you prefer for text analytics?

    We will prefer Python because of the following reasons:
    Python would be the best option because it has Pandas library that provides easy to use data structures and high-performance data analysis tools.

    R is more suitable for machine learning than just text analysis.

    Python performs faster for all types of text analytics.

  • What does P-value signify about the statistical data?

    P-value is used to determine the significance of results after a hypothesis test in statistics. P-value helps the readers to draw conclusions and is always between 0 and 1.

    • P- Value > 0.05 denotes weak evidence against the null hypothesis which means the null hypothesis cannot be rejected.
    • P-value <= 0.05 denotes strong evidence against the null hypothesis which means the null hypothesis can be rejected.
    • P-value=0.05is the marginal value indicating it is possible to go either way.
  • Can you write the formula to calculate R-square?

    R-Square can be calculated using the below formula :
    1 - (Residual Sum of Squares/ Total Sum of Squares)

  • What is the difference between type I vs. type II error?

    A type I error occurs when the null hypothesis is true, but is rejected. A type II error occurs when the null hypothesis is false, but erroneously fails to be rejected.”

  • Explain the 80/20 rule, and tell me about its importance in model validation.

    People usually tend to start with a 80-20% split (80% training set – 20% test set) and split the training set once more into a 80-20% ratio to create the validation set. It is used to verify the model performance on unseen data.

  • What is bias?

    Bias is an error introduced in your model because of the oversimplification of a machine learning algorithm." It can lead to underfitting.

  • What is Ensemble Learning?

    The ensemble is a method of combining a diverse set of learners together to improvise on the stability and predictive power of the model. Two types of Ensemble learning methods are:

    Bagging
    Bagging method helps you to implement similar learners on small sample populations. It helps you to make nearer predictions.

    Boosting
    Boosting is an iterative method which allows you to adjust the weight of an observation depends upon the last classification. Boosting decreases the bias error and helps you to build strong predictive models.

  • How is kNN different from kmeans clustering?

    Don’t get mislead by ‘k’ in their names. You should know that the fundamental difference between both these algorithms is, kmeans is unsupervised in nature and kNN is supervised in nature. kmeans is a clustering algorithm. kNN is a classification (or regression) algorithm.

    kmeans algorithm partitions a data set into clusters such that a cluster formed is homogeneous and the points in each cluster are close to each other. The algorithm tries to maintain enough separability between these clusters. Due to unsupervised nature, the clusters have no labels.

    kNN algorithm tries to classify an unlabeled observation based on its k (can be any number ) surrounding neighbors. It is also known as lazy learner because it involves minimal training of model. Hence, it doesn’t use training data to make generalization on unseen data set.

  • What is the difference between covariance and correlation?

    Correlation is the standardized form of covariance.
    Covariance's are difficult to compare. For example: if we calculate the covariance's of salary ($) and age (years), we’ll get different covariance's which can’t be compared because of having unequal scales. To combat such situation, we calculate correlation to get a value between -1 and 1, irrespective of their respective scale.

  • When does regularization becomes necessary in Machine Learning?

    Regularization becomes necessary when the model begins to ovefit / underfit. This technique introduces a cost term for bringing in more features with the objective function. Hence, it tries to push the coefficients for many variables to zero and hence reduce cost term. This helps to reduce model complexity so that the model can become better at predicting (generalizing).

  • Please enumerate the various steps involved in an analytics project.

    Following are the numerous steps involved in an analytics project:

    • Understanding the business problem
    • Exploring the data and familiarizing with the same
    • Preparing the data for modeling by means of detecting outlier values, transforming variables, treating missing values, et cetera
    • Running the model and analyzing the result for making appropriate changes or modifications to the model (an iterative step that repeats until the best possible outcome is gained)
    • Validating the model using a new dataset
    • Implementing the model and tracking the result for analyzing the performance of the same
  • Please explain Gradient Descent.

    The degree of change in the output of a function relating to the changes made to the inputs is known as a gradient. It measures the change in all weights with respect to the change in error. A gradient can also be comprehended as the slope of a function. Gradient Descent refers to escalating down to the bottom of a valley. Simply, consider this something as opposed to climbing up a hill. It is a minimization algorithm meant for minimizing a given activation function.

  • What is ROC curve?

    It stands for Receiver Operating Characteristic. It is basically a plot between a true positive rate and a false positive rate, and it helps us to find out the right tradeoff between the true positive rate and the false positive rate for different probability thresholds of the predicted values. So, the closer the curve to the upper left corner, the better the model is. In other words, whichever curve has greater area under it that would be the better model.

  • What is IQR ( Interquartile Range ) and Usage ?

    It is difference between 75th and 25th percentiles, or between upper and lower quartiles, It is also called Misspread data or Middle 50%.
    Mainly to find outliers in data, if the observations that fall below Q1 - 1.5 IQR or above Q3 + 1.5 IQR those are considered as outliers. Formula for IQR = Q3-Q1

  • What is Hypothesis Testing ?

    Hypothesis testing is a statistical method that is used in making statistical decisions using experimental data. Hypothesis Testing is basically an assumption that we make about the population parameter.

  • What type of metrics in Regression ?

    RMSE – Root Mean square error

    MSE – Mean square error

    MAE – Mean absolute Error

    R2 Square

  • What type of metrics in Classification ?

    Confusion Matrix = ((TP + FN)/(FP + TN))
    Accuracy score = (TP+TN)/TP+TN+FP+FN
    Recall , True positive rate, – ( TP/TP+FN)
    Precision – (TP/TP+TN)
    F1score = 2(precision*recall)/precision + recall

  • What is precision?

    The ration of predicted positive against the actual positive.
    It is the most commonly used error metric is n classification mechanism.
    The range is from 0 to 1, where 1 represents 100%.

  • What is recall?

    The ratio of the true positive rate against the actual positive rate.
    The range is again from 0 to 1

  • What is the difference between a dictionary and a set?

    Dictionary has key value pair
    set does not have key value pairs
    set has only unique elements

  • What are the various types of analysis on type of data?

    Univariate – 1 variable
    Bivariate – 2 variables
    Multivariate – more than 2 variables

  • What is the difference between qualitative & quantitative ?

    Quantitative method analyses the data based on numbers. Qualitative method analyses the data by attributes.

  • What are residuals in a regression task?

    The difference between the predicted value and the actual value is called the residual.

  • What is Back Propagation and Explain it’s Working.

    Back propagation is a training algorithm used for multilayer neural network. In this method, we move the error from an end of the network to all weights inside the network and thus allowing efficient computation of the gradient.

    It has the following steps:

    • Forward Propagation of Training Data
    • Derivatives are computed using output and target
    • Back Propagate for computing derivative of error with respect to output activation
    • Using previously calculated derivatives for output
    • Update the Weights

Why Techdata Solution ?

  • Industry savvy trainer with More than 10+ years experience with the technology.
  • Job oriented course design.
  • 120+ hrs of training session for the trainees.
  • 30+ hours of separate assignment and real time Project
  • 10+ Real Time Projects.
  • Limited Students per Batch, to enable more attention to every trainee.
  • Course Material Hard & Soft Copies with premium material and free lifetime access to recorded sessions.
  • Interactive learning at learner's convenience.
  • Customized Curriculum.
  • Resume preparation Discussion on real time interview question.
  • 24*7 support
  • 100% Placement assistance and Certificate would be awarded at the end of the program by Techdata Solution.

FAQS

  • Why should you choose Data Science?

    According to a data scientist, the analytic market which is growing every day requires approx 1,90,000 analytic professionals in order to tackle the exploding data across different verticals. Well, renowned companies like TCS, Infosys, Accenture, Oracle, Google, IBM, and Microsoft are looking for skilled and certified data analysts to hire a lot of data analysts.

  • Why should we learn R and Python for data science ? can't we learn any other tools ?

    R and Python has approximately 80% market share & both are open source (free of cost). Hence, R and Python both are very lucrative in the analytics space. R in built packages and python in built library provides the best combination to learn the concept like machine learning and deep learning artificial intelligence at one place. Almost all the jobs are asking for experience & exposure in R and Python.

  • Will I get Free demo before Enroll I for the Course ?

    Yes we arrange face to face meeting with our faculty.You can ask whatever queries you have related Training.

  • What if I miss a class?

    You will never miss a lecture at Techdata Solution You can choose either of the two options:
    1)View the recorded session of each and every topic
    2)You can attend the missed session, in any other live batch.

  • Will I get placement assistance?

    We Offer 100% Placement Assistance at Techdata Solution.
    We have corporate tie ups,after Completing training Techdata Solution Team process your profile to corporate to fetch relevant opportunities for you. 

  • What is the average pay scale for Data Scientist?

    The average pay scale of a data scientist is around 7 Lakhs per annum to 40 Lakhs per annum, depending upon how well skilled, and how much experience a person has in the field of data science. More the experience and skills, more the salary a person gets.

  • What if I have queries after I complete this course?

    You get 24x7 lifetime access to our Support Team who will help resolve your queries during and after the course 

  • How soon after signing up will I get access to the course content?

    After enrollment, you will get instant lifetime access to the Live classroom sessions. You will be able to access the complete set of previous class recordings, presentations, PDFs, assignments, etc. Moreover, you will immediately get access to our 24x7 support team so that you can start learning right away.

  • Is the course material accessible to the students even after the course training is over?

    Yes! All learners get lifetime access to all course material once you have enrolled.

  • What are the different modes of payment Available?

    Here are the different payment method

    • Cash
    • Net Banking
    • Cheque
    • Debit Card
    • Credit Card
    • Google Pay
    • Visa
    • Mastercard
    • American Express
    • Discover