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Data Science Training in Noida

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Maintain the project role and play a role in the IT company.

  • The course covers analyzing data from a variety of sources, learning machine learning techniques, and learning statistics.
  • The students will learn the roles and the skills involved in data science in this section. We will also teach you the specific steps involved in tailoring your data to so that it fits the target audience exactly.
  • The purpose of this section is to discuss various types of data analysis. As part of the field of data science, projects are executed, planned, and presented in a variety of ways. By utilizing these tools and techniques, your business can get the most from its data.
  • By the end of this course, you will be able to comprehend data science in depth. Data Scientists, Data Engineers, and Product Analysts are among the many fields covered in this lecture.
  • You will learn how to gather and analyze data using tools like R, Python, and the command line during this course. A/B testing and market analysis will be discussed, among other things.
  • Many well-known technology companies will be attending the event this year, including Amazon, Square, Facebook, Microsoft, Google, and Airbnb.
  • Additionally, the course provides answers to each question and a description of how it was answered. The program will assist you in preparing for exams, as well as at work.
  • Before attending an interview, you might find it helpful to familiarize yourself with the following topics.
  • These courses will provide students with an in-depth understanding of the subject matter. Students in our programs are prepared for job interviews, trained for employment, and provided with employment opportunities at reputable companies.
  • Concepts: Data Science, significance of Data Science in today’s digitally-driven world, components of the Data Science lifecycle, big data and Hadoop, Machine Learning and Deep Learning, R programming and R Studio, Data Exploration, Data Manipulation, Data Visualization, Logistic Regression, Decision Trees & Random Forest, Unsupervised learning, Association Rule Mining & Recommendation Engine, Time Series Analysis, Support Vector Machine - (SVM), Naïve Bayes, Text Mining, Case Study.
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Course Objectives

Data science requires the fundamentals of insights and math, which should be clear to have the option to break down the issues that are within reach. To take business issues, you need to have delicate skills like the group of executives and command over the activities to fulfill the time constraints. You will discover various data researchers with a four-year college education in insights and AI yet it's anything but a prerequisite to learning data science.

You need to have information on various programming languages, similar to Python, Perl, C/C++, SQL, and Java, with Python being the dominant basic cryptography language required in data science jobs. These programming languages work with data researchers to sort out unstructured information sets.

The important goal of Data Science is to remove significant data to be utilized in the key higher intellectual interaction, advancement, scientific reasoning, and articulation. The critical procedures being used are data handling, enormous data examination, data extraction, and data recovery. The reason for data science is to make the methods for removing business-centered infiltrations from the information.

Data science bunches have people from different foundations like substance designing, physical science, financial aspects, insights, math, research, innovation, and so on. You'll understand a few information researchers with a four-year college education in insights and AI anyway it is anything but an interest to be revealed to Data Science.

The different edges of Data Science are as per the following:
  • The plenty of Positions.
  • An amazingly Paid Career.
  • Data Science is adaptable.
  • Data Science Makes data higher.
  • Data researchers are very Prestigious.

With the right capabilities, you'll get delighted from a great vocation standpoint as a proficient soul. The interest for individuals with these abilities can in any case increase, and individuals effectively in information science jobs will undoubtedly see their pay rates increment inside what's to come. Data researchers work at stretches in most significant ventures any place development is happening. In addition to the fact that IBM predicted the interest for data researchers would develop by 28th in 2020.

  • Get settled with Python.
  • Learn data investigation, control, and perception with pandas.
  • Learn AI with script-learn.
  • Comprehend AI in extra profundity.
  • Continue to learn and dynamic.

Who is qualified to take this Data Science Certification Course?

A union of association, data science, and the right contraptions and development are fundamental for playing the limit of data science:
  • IT Professionals.
  • Banking and Finance Professionals.
  • Promoting Managers.
  • Assessment Managers.
  • Freshers.

What are the open positions in the track of completing the Data Science Training Course?

To name many, some of the chief regular occupation titles for data researchers include:
  • Business expert.
  • Data Mining Engineer.
  • Data creator.
  • Data Scientist.
  • Senior Data Scientist.

What abilities are used in a Data Science Training Course?

One of the first important specialized knowledge soul abilities is applied number-related examination and figuring, mining, and cycling enormous informational indexes. This moreover includes separating the data that is thought important. Some data researchers have a pH scale:
  • Measurements.
  • Programming Language R/Python.
  • Data Extraction, Transformation, and Loading.
  • Data fight and Data Exploration.
  • AI And Advanced Machine Learning (Deep Learning).

What purpose is it important to get intimate with a Data Science Training Course?

Data researchers wisdom to utilize their abilities in maths, measurements, programming, and option-associated subjects to plan goliath data sets. Then, at that point, they apply their insight to uncover arrangements wrapped up inside the data required on business difficulties and objectives. Data science is highly asked after and clarifies any way advanced data is rebuilding organizations and serving to them make chiseler and critical choices.

What should I learn about Data Science Certification Training Course?

If you prefer Python, for example, you should be intimate with libraries like pandas, NumPy, matplotlib or Plotly, and scikit-learn, and you should be satisfied with the cleaning, analyzing, and visualizing data using them. Writing SQL queries. Statistics data and systems. Fundamental machine learning and modeling abilities.

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Overview of Data Science Training in Noida

This Data Science Training in Noida course enables you to master data analysis, business analysis, data modeling, master algorithms for machine learning, clustering k-means, Naive Baye, etc. This course helps you to master R-statistical computing, develop an e-commerce recommendation engine and propose films and use retail basket analysis. Get the best data science training from top data researchers. Provide the most thorough and detailed data scientist training. The program has substantial input from industry specialists. The course curriculum. You will grasp several components, such as analytical data, mining, cleaning, data transformation, machine learning techniques, etc. Learn how technology is used for real-world data science issues and receive a deep insight into developing technologies, statistical analyses, and computer technology.

Additional Info

What is Data Science?

Data science combines multiple fields, together with statistics, scientific ways, AI (AI), and information analysis, to extract worth from information. Those that follow information science square measure are referred to as information scientists, and that they mix a variety of skills to investigate information collected from the net, smartphones, customers, sensors, and different sources to derive unjust insights.

Data science encompasses making ready information for analysis, together with cleansing, aggregating, and manipulating the information to perform advanced data analysis. Analytic applications and information scientists will then review the results to uncover patterns and modify business leaders to draw wise insights.

Why Data Science?

    Data science or data-driven science permits higher decision-making, prophetic analysis, and pattern discovery. It lets you :

  • Find the leading reason behind a drag by asking the proper queries
  • Perform explorative study on the information
  • Model the information exploitation numerous algorithms
  • Communicate and visualize the results via graphs, dashboards, etc.

    In the following, information science is already serving the airline business: predicting disruptions in visits alleviate the pain for each airline and passengers. With the assistance of knowledge science, airlines will optimize operations in some ways, including :

  • Plan routes and judge whether or not to schedule direct or connecting flights
  • Build prophetic analytics models to forecast flight delays
  • Offer customized promotional offers supported customers booking patterns
  • Decide that category of planes to get for higher overall performance
  • Prerequisites for Data Science

    Here are a number of the technical ideas you ought to realize before getting down to learning data science.

Data Science Modules :

Data Science is the backbone of information science. Knowledge Scientists ought to have a solid grasp of mil additionally to the basic information of statistics.

1. Modeling :

Mathematical models change you to form fast calculations and predictions supported by what you already realize. Modeling is additionally a district of mil and involves distinctive that algorithmic rule is the best suited to resolve a given drawback and the way to coach these models.

2. Statistics :

Statistics are at the core of information science. A durable handle on statistics will assist you to extract a lot of intelligence and procure a lot of purposeful results.

3. Programming :

Some level of programming is needed to execute a fortunate knowledge science project. The common programming languages are Python, and R. Python is particularly fashionable as a result of it’s straightforward to find out, and it supports multiple libraries for knowledge science and mil.

4. Databases :

As a capable knowledge individual, you wish to know how databases work, a way to manage them, and the way to extract knowledge from them.

How data science is transforming business?

    Organizations square measure victimization information science to show information into a competitive advantage by purification products and services. Information science and machine learning use cases include :

  • Determine client churn by analyzing information collected from decision centers, therefore promoting will take action to retain them
  • Improve potency by analyzing traffic patterns, climate, and different factors therefore supply corporations will improve delivery speeds and cut back prices
  • Improve patient diagnoses by analyzing medical take a look at information and according to symptoms, therefore, doctors will diagnose diseases earlier and treat them a lot of effectively
  • Optimize the availability chain by predicting once instrumentality can break down
  • Detect fraud in monetary services by recognizing suspicious behaviors and abnormal actions
  • Improve sales by making recommendations for patrons primarily based upon previous purchases
  • Many corporations have created information science a priority and square measure investment in it heavily. In Gartner’s recent survey of over 3,000 CIOs, respondents graded analytics and business intelligence because of the high differentiating technology for his or her organizations. The CIOs surveyed see these technologies because they are the most strategic for his or her corporations, and square measure investment consequently.

How data science is conducted?

The process of analyzing and acting upon information is unvaried instead of linear, however, this can be the info science lifecycle that usually flows for a knowledge modeling project :

1. Planning:

Outline a project and its potential outputs. Building a knowledge model: Information scientists typically use a spread of ASCII text file libraries or in-database tools to make machine learning models. Often, users can need the arthropod genus to assist with information consumption, information identification, and image, or feature engineering. They're going to like the proper tools additionally as access to the proper information and different resources, like computing power.

2. Evaluating a model :

Information scientists should reach a high proportion of accuracy for his or her models before they'll feel assured deploying them. Model analysis can usually generate a comprehensive suite of analysis metrics and visualizations to live model performance against new information and additionally rank them over time to alter the best behavior in production. The model analysis goes on the far side of raw performance to require into consideration expected baseline behavior.

3. Explaining models :

Having the ability to elucidate the inner mechanics of the results of machine learning models in human terms has not continuously been possible—but it's turning progressively vital. Information scientists need machine-controlled explanations of the relative coefficient and importance of things that get in generating a prediction, and model-specific instructive details on model predictions.

4. Deploying a model :

Taking a trained, machine learning model and obtaining it into the proper systems is commonly a tough and arduous method. This will be created easier by operationalizing models as ascendable and secure arthropod genus, or by exploiting in-database machine learning models. Monitoring models: Sadly, deploying a model isn’t the top of it. Models should always be monitored once in preparation to make sure that they're operating properly. The info the model was trained on could now not be relevant for future predictions once an amount of your time. As an example, in fraud detection, criminals square measure continuously turning out with new ways to hack accounts.

Tools for Data Science :

  • Building, evaluating, deploying, and watching machine learning models are often posh methods. That’s why there’s been a rise within the range of knowledge science tools. Information scientists use many sorts of tools, however, one in all the foremost common is open supply notebooks, that area unit internet applications for writing and running code, visualizing information, and seeing the results—all within the same surroundings.

  • Some of the foremost standard notebooks are Jupyter, RStudio, and Zeppelin. Notebooks are a unit helpful for conducting analysis however have their limitations once information scientists have to be compelled to work as a team. Information science platforms were designed to unravel this drawback.

  • To determine that the information science tool is correct for you, it’s necessary to raise the subsequent questions: What reasonable languages do your information scientists use? What reasonably operating ways do they prefer?

  • For example, some users value a knowledge supply-agnostic service that uses open source libraries. Others like the speed of in-database, machine learning algorithms.

Who oversees the data science process?

At most organizations, information science comes are generally overseen by 3 sorts of managers:

1. Business managers :

These managers work with the info science team to outline the matter and develop an analysis method. They'll be at the top of a line of business, like selling, finance, or sales, and have a knowledge science team news to them. They work closely with the info science and IT managers to confirm that comes are delivered.

2. IT managers :

Senior IT managers are to blame for the infrastructure and design which will support information science operations. They regularly observe operations and resource usage to confirm that information science groups operate with efficiency and firmly. They'll even be to blame for building and changing IT environments for information science groups.

3. Data science managers :

These managers manage the info science team and their daily work. Their team builders United Nations agency will balance team development with project coming up with an observance.

But the foremost vital player during this method is the information individual.

What is a data scientist?

As a specialty, information science is young. It grew out of the fields of applied math analysis and data processing. the info Science Journal debuted in 2002, revealed by the International Council for Science: Committee on information for Science and Technology. By 2008 the title of information man of science had emerged, and therefore the field quickly took off. There has been a shortage of information scientists ever since, although a lot of schools and universities have started providing information science degrees.

An information scientist’s duties will encompass developing ways for analyzing data, getting ready information for analysis, exploring, analyzing, and visualizing information, building models with information exploitation programming languages, like Python and R, and deploying models into applications.

The data man of science doesn’t work solo. The foremost effective information science is finished in groups. Additionally, to an information man of science, this team may embrace a business analyst WHO defines the matter, an information engineer WHO prepares the info and the way it's accessed AN IT creator WHO oversees the underlying processes and infrastructure, and an application developer WHO deploys the models or outputs of the analysis into applications and merchandise.

Challenges of implementing data science projects :

  • Despite the promised knowledge of science and large investments in data science groups, several corporations don't seem to be realizing the complete price of their information. In their race to hire talent and build information science programs, some corporations have become intimate with inefficient team workflows, with totally different folks victimizing different tools and processes that don’t work well alone. While not a lot of disciplined, centralized management, executives may not see a full come back on their investments. This chaotic atmosphere presents several challenges.

  • Data scientists can’t work with efficiency. As a result of access to information should be granted by the Associate in Nursing IT administrator, information scientists usually have long waits for information and therefore the resources they have to research it. Once they need access, the information science team would {possibly} analyze the information victimization differently—and possibly incompatible—tools. As an example, a soul may develop model victimization of the R language, however, the application it'll be utilized in is written in a very completely different language.

  • Application developers can’t access usable machine learning. Typically the machine learning models that developers receive don't seem to be able to be deployed in applications. And since access points are inflexible, models can’t be deployed altogether, and measurability is left to the applying developer.

  • IT directors pay an excessive amount of time on support. Thanks to the proliferation of open supply tools, IT will have an Associate in Nursing ever-growing list of tools to support. a knowledge soul in promoting, as an example, could be victimizing completely different tools than a knowledge soul in finance. Groups may additionally have completely different workflows, which suggests that IT should regularly reconstruct and update environments.

  • Business managers are too far away from information science. Information science workflows don't seem to be forever integrated into business decision-making processes and systems, creating it tough for business managers to collaborate knowledgeably with information scientists. While not higher integration, business managers notice it tough to know why it takes to see you later to travel from epitome to production—and they're less doubtless to back the investment incomes they understand as too slow.

Benefits of Data Science Platform :

    A data science platform reduces redundancy and drives innovation by sanctionative groups to share code, results, and reports. It removes bottlenecks within the flow of labor by simplifying management and incorporating best practices. In general, the simplest knowledge science platforms aim to :

  • Make knowledge scientists additional productive by serving to them accelerate and deliver models quicker, and with less error
  • Make it easier for knowledge scientists to figure with giant volumes and kinds of knowledge
  • Deliver trusty, enterprise-grade computer science that’s bias-free, auditable, and consistent
  • Data science platforms square measure designed for collaboration by a variety of users as well as knowledgeable knowledge scientists, subject knowledge scientists, knowledge engineers, and machine learning engineers or specialists. For instance, a science platform would possibly enable data scientists to deploy models as arthropod genus, creating it simple to integrate them into completely different applications. Knowledge scientists will access tools, data, and infrastructure while not having to attend for IT. The demand for knowledge science platforms has exploded within the market. The platform market is predicted to grow at a combined annual rate of quite 39 percent over the subsequent few years and is projected to reach 385 billion.

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Key Features

ACTE Noida offers Data Science Training in more than 27+ branches with expert trainers. Here are the key features,
  • 40 Hours Course Duration
  • 100% Job Oriented Training
  • Industry Expert Faculties
  • Free Demo Class Available
  • Completed 500+ Batches
  • Certification Guidance

Authorized Partners

ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.


Syllabus of Data Science Course in Noida
Module 1: Introduction to Data Science with R
  • What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.
  • Hands-on Exercise - Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
Module 2: Data Exploration
  • Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.
  • Hands-on Exercise -Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.
Module 3: Data Manipulation
  • Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.
  • Hands-on Exercise -Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
Module 4: Data Visualization
  • Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.
  • Hands-on Exercise -Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
Module 5: Introduction to Statistics
  • Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.
  • Hands-on Exercise -– Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
Module 6: Machine Learning
  • Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.
  • Hands-on Exercise -Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.
Module 7: Logistic Regression
  • Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression
  • Hands-on Exercise -Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
Module 8: Decision Trees & Random Forest
  • What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.
  • Hands-on Exercise -Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
Module 9: Unsupervised learning
  • What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.
  • Hands-on Exercise -Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.
Module 10: Association Rule Mining & Recommendation Engine
  • Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.
  • Hands-on Exercise -Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
Module 11: Introduction to Artificial Intelligence (self paced)
  • introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
Module 12: Time Series Analysis (self paced)
  • What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.
  • Hands-on Exercise -Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.
Module 13: Support Vector Machine - (SVM) (self paced)
  • Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
Module 14: Naïve Bayes (self paced)
  • what is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
Module 15: Text Mining (self paced)
  • Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.
Module 16: Case Study
  • This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
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Hands-on Real Time Data Science Projects

Project 1
Image Caption Generator Project

Image caption generator is a task that involves computer vision and natural language processing concepts to recognize the context of an image and describe them.

Project 2
Crime Analysis Project

Predict the crime rate and analyze the crime rate to be happened in future based on this Information the officials can take charge and try to reduce the crime rate.

Project 3
Boston Housing Predictions Project

The goal of this project is to create a regression model that is able to accurately estimate the price of the house given the features and made for predicting.

Project 4
Sentiment Analysis project

Objective Sentiment classification is a way to analyze the subjective information in the text and procedure by which information is extracted from the opinions.

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  • Our placement Team collaborates with the presumed associations for masterminding grounds interviews for the applicants. We put forth attempts to arrange specialized classes, workshops and corporate assumption meetings. Industry work force are welcomed occasionally to enhance the information on our applicants local area with the most recent mechanical advancements and industry rehearses.
  • Work with employees, division heads and organization to coordinate vocation arranging with scholarly educational program and placement.
  • Create mindfulness in the applicants in regards to future profession alternatives to place in top Mncs.
  • Data Science Training in Noida has helped understudies with getting their dream occupations in associations like IBM , HCL , Wipro , TCS , Accenture, ,etc.
  • We will send your resumes to associations work you land position, we completely support work you land your dream position.
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About Skillful Data Science Instructor

  • Our Data Science Training in Noida. Trainers offers a learner to get learning experience that is advantageous, time-delicate and spending plan agreeable. Best of all, you will zero in on the main thing of all learners .
  • Our ACTE group are fit for dealing with little to huge scope training prerequisites regardless of whether it is to run a start to finish training programs from introductory methodology to go live, or to help deal with a whole preparing measure beginning to end, our group of training specialists can assist learner with accomplishing learner preparation objectives continuously and assist learner business with augmenting learner return on initial capital investment.
  • Tutors give the preparation needs of our learners who look to us to give a scope of administrations and answers for their preparation needs on request premise.
  • ACTE training occurs inside a live climate, where the tutors and applicants communicate (through voice, screen sharing and offer notes) utilizing web conferencing apparatuses like Webex and Goto meeting.
  • Our coaches give through a mix of new and existing web advances, we bestow the Best Data Science Training to the learner.
  • Instructors to partake in our web based preparing program learners need to have great Web association and headset. Since voice is incorporated in our web conferencing programming, you will actually want to tune in and converse with the Learners through your PC coordinated speakers/headsets. webcams are not needed.

Data Science Course Reviews

Our ACTE Noida Reviews are listed here. Reviews of our students who completed their training with us and left their reviews in public portals and our primary website of ACTE & Video Reviews.



ACTE is the best training institute for Data science and Data Analytics in BTM Layout. The trainers are well experienced and the methodology of teaching is top notch. They provide practicals along with theoretical sessions for complete understanding of the concepts. They even provide placement assistance after course completion as well.


Software Engineer

Real time expert is on of the ACTE institute in Noida .Here teachers are very friendly and so good. A lot of student comes here to Learn Data Science and get is really a very good institute.??


Best DATA SCIENCE training institute in Tambaram with Realtime client projects and dedicated support team. I have taken Data science training on this January and completely happy with their teachings, projects and job support after the course completion. It's a One stop destination for your data science And AI training in BTM Layout.



ACTE for your career switch to Data Science..They have well experienced Trainers in ACTE who can make you industry ready. Curriculum is quite unique and includes current industry needs. They give very good job assistance also.


Software Engineer

Its a good institute for Data Science in Porur. The teaching staff is good, they give us day wise assignments which helped me to hands on algorithms of machine learning and also provide us the backup classes. The access they provide is very helpful to listen the classes repeatedly. They provide good placement assistance. Thanks social ACTE team for your support and guidance.

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Data Science Course FAQs

Looking for better Discount Price?

Call now: +91 93833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    • Gives
    • For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • The entire Data Science training has been built around Real Time Implementation
  • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
  • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

Why Should I Learn Data Science Course At ACTE?

  • Data Science Course in ACTE is designed & conducted by Data Science experts with 10+ years of experience in the Data Science domain
  • Only institution in India with the right blend of theory & practical sessions
  • In-depth Course coverage for 60+ Hours
  • More than 50,000+ students trust ACTE
  • Affordable fees keeping students and IT working professionals in mind
  • Course timings designed to suit working professionals and students
  • Interview tips and training
  • Resume building support
  • Real-time projects and case studies
Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Data Science batch to 5 or 6 members
Our courseware is designed to give a hands-on approach to the students in Data Science. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
You can contact our support number at +91 93800 99996 / Directly can do by's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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