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Data Science Training in Gurgaon

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

  • In this course, students learn how to analyze data at vast scales, perform machine learning and statistical analysis, and work with data from various sources.
  • After this introduction, we will look at the roles and skills required for data science. The course also covers web APIs and scraping, so you'll learn how to adapt your data to fit the needs of different users.
  • A method for analyzing data effectively will also be discussed. Students are introduced to a variety of methods for planning, executing, and presenting data science projects. Getting started in data science and maximizing the use of your data can be achieved using these tools and techniques.
  • You will gain a very comprehensive understanding of data science from this course. We'll discuss a variety of careers in the field, including Data Scientist, Data Engineer, and Product Analyst.
  • In this course, you'll be introduced to tools such as R, Python, and the command line so you can analyze data. In addition to several other topics covered in the course, participants will also discuss A/B testing and market analysis.
  • Several leading technological companies will participate in this year's event, including Amazon, Square, Facebook, Microsoft, Google, and AirBnB.
  • All course questions are provided with explanations and solutions, including explanations for the quizzes. Besides being a useful tool to prepare for exams, the program also serves as a resource while you are working.
  • In an interview, you may find it useful to know the following topics.
  • With this curriculum, students will have the opportunity to gain a deeper understanding of the subject matter. By completing our program, students have the opportunity to prepare for interviews or obtain employment 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.
  • Classroom Batch Training
  • One To One Training
  • Online Training
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This is How ACTE Students Prepare for Better Jobs


Course Objectives

There is loads of scope within the field of Data Science in India. In step with so, there are quite 2000 openings for the Data Science profile. Also, the typical salary of a theory mortal in India is ₹ 835,000. The salary might rely on your years of expertise. If you're having expertise of 3yrs or quite that then your payment would be around ₹ 18,50,000 and it'll continue increasing in step with the expertise. The role of a Data Scientist is currently a buzzworthy career. It's endurance within the marketplace and provides opportunities for people that study Data science to create valuable contributions to their corporations and societies at monster.

Entry-level data scientist salaries are as motivating because of the job itself. If you'll be able to crack Amazon data science internship or Google data science internship, the knowledge you'll gather here will give a foothold to your career, and so there would be no seeing back. Data science jobs are the review of the city! A data science internship is one good idea to get the area as well as to get the first-hand experience in this area. Several final year graduate seniors look ahead to a job in this new-age area.

  • As with most professions, salaries for Data Scientist vary wide supported or her years of expertise.
  • Salary data from Glassdoor indicates that the typical base procurers Data Scientist is ₹ 8,24,100 yearly, and therefore the average base procurers senior Data Scientist is ₹698,412 yearly.

Now is that the time to start your career in data science! Data science is the most passionate career to make this year. You will be learning several tools, like sequel Python Hadoop and even data storytelling, all of which form up the entire data science pipeline.

  • Since prime corporations don’t sometimes like freshers for these roles, expertise in real-time Data science projects is going to be another advantage.
  • Our training is concentrated on covering all basic curriculum with Data Science besides real-time workplace exercises.
  • Our live projects can help you to know the subject in an intensive method and prepare you to face interviews with top corporations for Data science roles.
  • Our trainers can share with you their expertise and guide you to unravel period issues.
  • Either online or room training is obligatory to start a career in Data Science.
Today, we will examine Software testing abilities that are fundamental for any driving bundle analyzer:
  • Programming: Python, SQL, Scala, Java, R, MATLAB.
  • Machine Learning: Natural Language Processing, Classification, Clustering.
  • Data Visualization: Tableau, SAS, D3.js, Python, Java, R libraries.
  • Big data platforms: MongoDB, Oracle, Microsoft Azure, Cloudera.
As a job chance for Java confirmed, anybody will mull over the ensuing position jobs:

You need to own information of varied programming languages, like Python, Perl, C/C++, SQL, and Java, with Python being the most common committal to writing language needed in Data science roles. These programming languages facilitate Data scientists to organize unstructured information sets.

What are the purposes of our Data Science training?

Here are the various powerful tips for anyone who has to begin learning Java:
  • We tend to style the curriculum as per the newest trade standards.Our trainers offer real-time training with living projects which can assist you to own a deeper understanding of the topic.
  • We provide the training in various modes like online, room on each weekday and weekend batches.
  • Our training schedule is incredibly versatile to fit your on-the-market timings.
  • Our Data science course can assist you to crack-related interviews in prime MNCs since our trainers can guide you throughout the method by sharing their real-time expertise in relevant fields.

Who Should take this Data Scientist Master’s program?

The Data Science role needs an amalgam of expertise, Data Science knowledge, and proper tools and technologies:
  • IT Professionals
  • Banking and Finance Professionals
  • Marketing Managers
  • Analytics Managers

How do I begin a profession in Data Science?

Essential Advice for People beginning a Career in Data Science:
  • Choose the proper role.
  • Take up a Course and Complete it.
  • Choose a Tool / Language and continue it.
  • A Generation.
  • Focus on possible applications and not only theory.
  • The proper resources.
  • Go on your communication skills.

Why do I have to Learn Data Science to create your career?

It is a common basic skill for a Data Science professional. Understanding data storage techniques along with the basics of big data will make you much more desirable than a person which hi-fi words on the resume, it's because companies are still thinking about their data science fundamentals. Data scientists use their skills in maths, statistics, programming, and different connected subjects to arrange giant information sets. Then, they apply their knowledge to uncover solutions hidden within the data to require business challenges and goals.

How do I start Data Science from scratch?

How to step into Data Science as a whole beginner:
  • Learn the fundamentals of programming with Python.
  • Get basic Statistics and arithmetic.
  • Learn Python for Data Analysis.
  • Learn Machine Learning.
  • Repeat with projects.
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Overview of Data Science Training in Gurgaon

ACTE offers a thorough Data Science course for beginners that will teach you how to use Python to create data science applications and tools. In this course, you'll learn the language via a combination of theory and practise, so you'll be prepared to meet the rising demand for data scientists. It also provides students with hands-on experience that will help them better grasp the real-world needs of data analysis. Our academy provides teaching at a reasonable price by well-qualified and certified trainers.


Additional Info

How Does Data Science Work?

To produce a holistic, thorough and refined look at raw data, data science involves a variety of disciplines and expertise areas. To be able to effectively sort through the muddled mass of information and communicate only the ingredients that will drive innovation and efficiency, data scientists have to be skilled in everything from data engineering, math, statistics, and advanced computing. Using algorithms and other techniques, data scientists also rely heavily on artificial intelligence, especially in its subfields of machine learning and deep learning.

Data science generally has a five-stage lifecycle that consists of:

Capture:- Data acquisition, data entry, signal reception, and data extraction

Maintain:- The process of storing, cleaning and staging data, and analyzing data.

Process:- Mine data, classify data, model data, summarize data

Communicate:- Reporting, analysis and visualization of data, business intelligence, decision-making

Analyze:- Exploratory and confirmatory, predictive, regression, text mining, and qualitative analyses

Main Components of Data Science:

The main components or processes are as follows:

1. Data Exploration:- The most important step is the one that takes the most time. The majority of time spent on data exploration is spent on finding patterns and trends. It is rare that data that we obtain is in a correct structured form, which is a main ingredient for data science. The data contains a lot of noise. There is too much data here that isn't required and is therefore noise. In this step, what should we do? We sample and transform our data in this step, in order to identify observations (rows) and features (columns), and to remove the noise by using statistical methods. We also use this step to determine whether there are missing values in the data set as well as to evaluate the relationship between various features (columns). By this, we mean if the features (columns) are dependent on each other or independent of each other. Data is basically prepared for further use after it has been transformed. As a result, this is a very time-consuming process.

2. Modeling:- We have now prepared and prepared our data. Using Machine Learning algorithms is the second step in this process. Adapting data to a model is what we do here. Data type and business requirements determine the model to use. Choosing the right model for recommending an article to a customer is not the same as the model that is required for predicting sales on a given day. We fit the data into the model once the model has been decided.

3. Testing the Model:- The next step in the modeling process is important, especially for performance. Testing the model with test data allows it to be checked for accuracy, characteristics, and other changes required to get the desired result. In case accuracy is not achieved, we may go back to step 2 (modelling) and select a different model, then repeat the same step 3 and choose the best model that suits the business needs.

4. Deploying Models:- By properly testing a model as per business requirements, we get the desired result. Once the model has been finalized and tested, we deploy it into a production environment.

Characteristics of Data Science:

The characteristics are as follows:

1. Business Understanding:- This is your most important characteristic, because without an understanding of the business you will not be able to make a good model, regardless of your mechanical or statistical abilities. Developing analytics in accordance with the business requirements is the responsibility of a data scientist. As a result, business knowledge is also important or helpful.

2. Intuition:- A data scientist needs to choose the right model with the right accuracy since all models will not produce the same results although the math involved is proven and foundational. So, a data scientist must understand when a model is ready to be deployed in production. A production model needs the intuition to know when it is stale and must be reengineered to respond to a changing business environment.

3. Curiosity:- The field of data science is not new. It has also appeared in the past, but the pace at which it is being developed is very fast. A data scientist's curiosity to learn emerging technologies becomes very important since new methods to solve familiar problems are constantly being developed.

The 8 Data Science Skills That Will Get You Hired

Programming Skills:- The tools of the trade are important no matter what type of company or role you're interviewing for. An R/Python-like statistical programming language, along with a SQL-like database query language, are needed.

Statistics:- Being a data scientist requires a deep understanding of statistics. Statistics, distributions, maximum likelihood estimates, and so on should be familiar to you. Machine learning will require a very similar level of statistics knowledge, but another crucial part is being able to recognize when specific techniques are (or aren't) applicable. Data-driven companies in particular depend on statistics to make decisions and design / evaluate experiments. Statistics are important at any company, but especially at those driven by data.

Machine Learning:- There may be situations where you will need to be familiar with machine learning methods if you're at a company with extensive data, or if you work at a company whose products are particularly based on data (e.g. Netflix, Google Maps, Uber). It could be anything from k-nearest neighbors, to random forests, to ensemble methods, and the like. There are a lot of these techniques you can implement with R or Python libraries, so you don't need to be an expert in how they work. Understanding the broad strokes and knowing when to use different techniques is more important.

Multivariable Calculus & Linear Algebra:- In companies whose products are defined by data, these concepts are of particular importance, and small improvements in algorithm performance or predictive performance can yield big rewards. You may be asked to explain how you came to conclusions from machine learning or statistics during an interview for a data science role. You might be asked a few basic mathematical questions, since multivariable calculus and linear algebra are crucial to many of this stuff. Many out-of-the-box Python or R implementations of these concepts are available, so you might wonder why a data scientist would need to understand them.

Data Wrangling:- Frequently, the data you analyze will be messy and hard to deal with. Considering this, it is really important to understand how to cope with imperfect data. Missing values, inconsistent string formats (such as New York versus New York versus Ny), and date formats (2017-01-01 versus 01/01/2017, unix time versus timestamp, etc.) are some examples of data imperfections. A skill like this is most important for those joining small companies when they're early employees or those working in data-driven companies where the product is not data-dependent (especially since the latter has typically grown quickly with little attention paid to data quality), but it's essential for anyone.

Data Visualization & Communication:- A good way to visualize and communicate data is extremely important, especially with young companies that are beginning to make data-driven decisions for the first time. Specifically, it means explaining to non-technical and technical audiences how your findings work and how the techniques work. When it comes to visualization, knowing tools like matplotlib, ggplot, or d3.js can prove immensely helpful. In addition to being popular for data visualisation, Tableau is also used for dashboards. Knowing how to visualize data is important, but so is understanding how to visually encode data and communicate it.

Software Engineering:- It's important to have a strong background in software engineering if you're interviewing at a smaller company. In addition to handling lots of logging and possibly developing data-driven products, you will be necessary to handle a lot of data.

Data Intuition:- Employers want to see you are a problem-solver who uses data. The interview process will probably include some questions about some high-level problem, such as a test or a data-driven product the company wants developed. Consider the most important things and discard the less important ones.

Top Frameworks used by Data Scientists:

Here are 10 open source machine learning frameworks available on the market, which are reportedly the most used by data science professionals.

1. TensorFlow:- A wide range of prominent brands, including Gmail, Uber, Airbnb, Nvidia, and others, utilize Tensorflow, a machine learning library for numerical computing developed at Google. Graphs, SQL tables, and images can be integrated via its formulation to create and experiment with deep learning architectures.

2. Scikit-learn:- Python programmers use Scikit-learn's open-source machine learning library to build their models. Combined with the frequent updates to improve performance and the fact that it's open-source, it's an industry favorite for machine learning.

3. Keras:- A Python library to build neural networks, KERAS is open-source. Several popular lower-level libraries are compatible with it, including Tensorflow, Theano & CNTK. Those who have a lot of data or seek the latest in artificial intelligence might find this to be their new best friend: deep learning.

4. Pandas:- Pandas is an open-source data manipulation and analysis library written in the Python programming language. The program offers data structures as well as operations that enable you to work with numerical tables and time series. In Pandas, incomplete, unlabeled, and messy data can be reshaped, merged, reshaped, and sliced using a variety of tools.

5. Spark MLib:- Machine learning libraries like Spark MLib are popular. The library is used by almost 6% of data scientists, according to a survey. Java, Scala, Python, and R are all supported by this library. The library can also be used on Hadoop, Apache Mesos, Kubernetes, and other cloud services.

6. PyTorch:- Tensorflow has been superseded by PyTorch as the most popular deep learning software tool at Facebook. The PyTorch library operates with dynamically updated graphs, unlike TensorFlow. Changing the architecture is possible during this process.

7. Matplotlib:- A Python plotting library, Matplotlib is also used for numerical extensions to Numpy and is primarily used for data visualization through histograms, scatterplots, and 3D plots. It is the visualization library of choice for all Python data science test cases since it produces histograms, scatterplots, 3D plots, image plots, bar charts, power spectra, and more.

8. Numpy:- The open-source library Numpy provides programmers with the flexibility to work with arrays and matrices. Fortran is a powerful tool that assists in integrating C and C++ code with Python. Check out the NumPy tutorial and examples for NumPy.

9. Seaborn:- It is based on the matplotlib package and provides Python data visualization capabilities. Visualizing statistical models is the main focus of this package. Heat maps are visual displays that summarize data while still depicting the overall distributions.

10. Theano:- Analogous to Numpy, the Theano Python library performs numerical computations. Python 2 uses Theano as its base component for doing mathematical computations. Mathematical expressions involving multi-dimensional arrays can easily be defined, optimized, and evaluated using Theano

Advantages of Data Science:

Data Science has several benefits, including the following:

1. It’s in Demand:- There is a great deal of demand for data scientists. Those seeking employment have many opportunities at their disposal. In 2026, 11.5 million jobs are expected to be created in the field, the fastest growing job on Linkedin. It is therefore regarded as an extremely employable job sector.

2. Abundance of Positions:- Data Scientists need a unique skill-set, and very few people possess it. Due to this, Data Science differs from other IT sectors in that it is less saturated. Consequently, Data Science offers a lot of opportunities and is a vast field of study. Despite high demand for Data Scientists, the number of Data Scientists available is low.

3. A Highly Paid Career:- There are few professions that pay as much as data science. A Data Scientist makes on average $116,100 a year, according to Glassdoor. This makes Data Science an appealing career choice.

4. Data Science is Versatile:- The data science field has many applications. Several industries use it, including health-care, banking, consultancy services, and e-commerce. Data Science has many applications. Thus, you will be able to work in different fields.

5. Data Science Makes Data Better:- Performing data processing and analysis requires the expertise of Data Scientists. Additionally, they improve the quality of data as well as analyze it. Therefore, Data Science entails enriching data to serve the needs of the company.

6. Data scientists are in high demand:- Companies that hire Data Scientists are able to make more informed business decisions. They are employed by companies in order to provide their clients with better results through their expertise. This position in the company gives Data Scientists a great deal of responsibility.

7. No more monotonous tasks:- Various industries have benefited from data science by automating redundant tasks. In order to perform repetitive tasks, companies train machines by using historical data. As a result, humans no longer have to perform the arduous jobs previously performed by humans.

8. Data Science Makes Products Smarter:- Using Machine Learning, Data Science has enabled industries to create better-tailored products to better serve customers. Websites that use Recommendation Systems to provide personalized insights to users are popular among e-commerce websites. Data-driven decisions can now be taken by computers based on human behavior.

9. The power of data science is life-saving:- Because of Data Science, the healthcare industry has greatly improved. Detecting early-stage tumors has become easier with the advent of machine learning. Other sectors of the health care industry are also using data science to assist their clients.

10. Data Science Can Make You A Better Person:- In addition to helping you build a successful career, Data Science will also help you grow personally. A problem-solving attitude will be developed in you. The best of both worlds is possible in Data Science roles since they bridge IT and Management.

Data Science Training certifies you with ‘in demand’ Big Data Technologies.

Data Science Training is the best way to prepare for the growing demand for skills and technologies relating to Big Data. Professionals are equipped with data management technologies such as Hadoop, R, Flume, Sqoop, Machine learning, Mahout, and more. This adds value to their careers and makes them more competitive. Having mastered data sciences and Big Data, you will be able to get high-paying jobs in the Data Science industry. You can also get the top-paying Big Data job title after you complete this training. There are numerous job titles offering handsome salaries in IT that are related to Big Data and Data Science. Today, Big Data and Data Science have spread across all leading industries and not just in the IT field. So, it becomes evident that a certified Data Science Professional has no limit to what they can accomplish.

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Key Features

ACTE Gurgaon 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 Gurgaon
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
Stock Market Prediction Project

In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company and recent trend in stock market prediction technologies.

Project 2
Fake News Detection Project

The goal of this project is to find the effectiveness and limitations of language-based techniques for detection of fake news through the use of machine learning algorithms.

Project 3
Video Classification Project

This will help in building a strong recommendation system, which will recommend a video to a learner based on his learning style and professor's teaching style in the video.

Project 4
Human Action Recognition Project

Efficient and effective representation of streamed body pose data which can be used generically for understanding and analysing the actions of the body.

Our Engaging Placement Partners

ACTE Gurgaonis certify around the world. It expands the worth of your resume and you can accomplish driving position posts with the assistance of this affirmation in driving MNC's of the world. The certificate is just given after fruitful finishing of our preparation and pragmatic based undertakings.
  • We have HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM, and others are among our partners. It enables us to position our students in top multinational corporations around the world.
  • ACTE is the world pioneer in giving position help to understudy with the help of a submit plan cell that assistance and helps understudies during the hour of circumstance.
  • Mock meets by placement team give learner the stage to plan, practice and experience the genuine new delegate screening. changing with the get-together climate effectively in a free and quiet climate gives learner an edge over learner colleagues.
  • We have more than 1000+ enrollment expert illuminating overview who dependably select new graduated class.
  • We have a submitted system support pack wing that help understudies in getting condition as per their necessities.
  • ACTE has bound with different assurance working environments and affiliations furthermore is a force extra of a recognizable occupation entryway in India.

Get Certified By MCSE: Data Management and Analytics & Industry Recognized ACTE Certificate

Acte Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher's as well as corporate trainees. Our certification at Acte is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC's of the world. The certification is only provided after successful completion of our training and practical based projects.

Complete Your Course

a downloadable Certificate in PDF format, immediately available to you when you complete your Course

Get Certified

a physical version of your officially branded and security-marked Certificate.

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About Qualified Data Science Trainer

  • Our Data Science Training in Gurgaon. At ACTE, learner will obtain from the specialists from industry who are energetic in offering their insight to learners. get truly coordinated by the prepared experts.
  • Our tutors help the learners in building their resume expertly and in addition support their confirmation by giving huge snippets of data to them about demands questions.
  • As all Coaches are Data Science training working subject matter experts so they are having various live activities, guides will utilize these tasks during educational courses.
  • Our guide gives ideal mix of hypothetical and accommodating preparing to make you industry competent.
  • Trainers are also help applicants to get placed in their respective company by Employee Referral / Internal Hiring process.
  • We gives whenever lab working conditions applicants are permitted to get to the labs for perpetual number of hours as per their own maintained timings.

Data Science Course Reviews

Our ACTE Gurgaon 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

I have done Data Science course in Real Time Experts. Initially we are lucky to have a good Trainers in ACTE . He is a good trainer. He taught us from the basics. He is very friendly & shared his knowledge & motivate us in Gurgaon..


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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