Data Science Course in Bhubaneswar | Best Certified Training | Placement
Home » Data Science & Ai Courses India » Data Science Training in Bhubaneswar

Data Science Training in Bhubaneswar

(5.0) 6987 Ratings 7089Learners

Live Instructor LED Online Training

Learn from Certified Experts

  • Novices & Excellent level Courses.
  • Most unique Training toward meeting Training Methods into Data Science.
  • Existence Path to student Portal, Study Materials, Videos & Top MNC Conference Question.
  • Reasonable Prices beside Best curriculum Planned on Industrial Data Science Expert.
  • Worked over 9+ years as regarding Data Science Certified Specialist.
  • Following Data Science Batch to Open here a week– Join Your Sign Immediately!

aws training


INR 18000

INR 14000


INR 22000

INR 18000

Have Queries? Ask our Experts

+91-8376 802 119

Available 24x7 for your queries

Upcoming Batches

25- Sep- 2023

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

27- Sep- 2023

Weekdays Regular

08:00 AM & 10:00 AM Batches

(Class 1Hr - 1:30Hrs) / Per Session

30- Sep- 2023

Weekend Regular

(10:00 AM - 01:30 PM)

(Class 3hr - 3:30Hrs) / Per Session

30- Sep- 2023

Weekend Fasttrack

(09:00 AM - 02:00 PM)

(Class 4:30Hr - 5:00Hrs) / Per Session

Hear it from our Graduate

Learn at Home with ACTE

Online Courses by Certified Experts

Play a design role and participate in IT companies.

  • The course covers the basics of data science, including machine learning, statistical analysis, and how to work with data at vast scales.
  • Following this introduction, we will look at the different roles and skills that go into the data science process. You'll learn how to get data from different sources, such as web APIs and web scraped pages, later in the course.
  • There will also be a discussion on an effective method of data analysis. Students will be able to learn about a variety of approaches to planning, executing, and presenting data science projects. You can use these tools and techniques to get started with data science and improve your data utilization.
  • Through this course, you will be equipped with a comprehensive toolkit for a career in data science. A variety of careers in the field will be discussed, including Product Analyst, Data Engineer, Data Scientist, and more.
  • You'll learn how to use tools such as R, Python, and the command line to perform analysis of data. There will also be a discussion of A/B testing and market analysis among the several topics covered in the course.
  • Many renowned technology companies will contribute to the event, including Amazon, Square, Facebook, Microsoft, Google, and AirBnB.
  • All questions in the course will be covered in detail, including explanations and solutions. In addition to helping you to prepare for exams, the program can also serve as a reference while you are at work.
  • Knowing the following topics can help you succeed in your interview. This curriculum will allow students to gain a deeper understanding of the topic. Students may prepare for interviews and obtain employment at reputable companies by completing our program.
  • 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
  • Customized Training
  • Enroll Now

This is How ACTE Students Prepare for Better Jobs


Course Objectives

  • Using the NumPy and Scikit-Learn packages, you can learn about mathematical computing.
  • Recognize the various components of the Hadoop ecosystem.
  • Learn how to work with HBase, its architecture, and data storage, as well as the differences between HBase and RDBMS, and how to divide data using Hive and Impala.
  • Learn about MapReduce and its features, as well as how to use Sqoop and Flume to consume data.
  • Master the ideas, techniques, and applications of machine learning by learning about recommendation engines and time series modeling.
  • Learn how to use Tableau to do data analysis and create interactive dashboards.
This Data Science certification course comprises 15+ real-world, industry-based projects across many domains to assist you in mastering Data Science and Big Data topics. Listed below are a handful of the projects on which you will be working.
Python is the most frequent coding language required in data science positions, however other programming languages such as Perl, C/C++, SQL, and Java are also necessary. Data scientists can use these programming languages to manage unstructured data sources.
With data science becoming increasingly important to many organizations' overall goals, it's worth considering if data science certifications are required to work as a data scientist. In other words, it's a profession that necessitates a wide range of abilities (and, depending on the company and position, a lot of experience).
Azure AI Fundamentals is a Microsoft Certified program.
  • Azure Data Scientist Associate is a Microsoft certification.
  • Certified Data Scientist in the Open (Open CDS)
  • SAS AI & Machine Learning Professional certification.
  • SAS Big Data Professional certification.
  • SAS Data Scientist certification.
Data science is a large area, and it is impossible to become an expert in it in six months or a year. To get started with data science, you'll need specific technical abilities as well as a fundamental understanding of programming and analytics tools. This Data Science course, on the other hand, covers all of the necessary topics from the ground up, making it simple to put your new abilities to use.
Because this is a time-consuming procedure, businesses are ready to pay top dollar for data scientists and data engineers. “Demand for data scientists is at an all-time high, and supply hasn't been able to keep up, resulting in a severe skill scarcity.

Who is eligible for Data Science Certification Course?

Students become specialists in implementing Data Science techniques in the real world based on their subject understanding of statistics, machine learning, and programming. Students from other fields, such as business studies, are also able to take Data Science courses.

How many programming languages will I learn in this Data Science Course?

This Data Science certification course will teach you Python, R, and Scala computer languages, as well as data science technologies including Apache Spark, HBase, Sqoop, Hadoop, and Flume.

What type of jobs will I be suited for after completing this Data Science course?

You will have the abilities necessary to get your ideal career in Data Science if you finish the Data Science certification course. Professionals with a background in data science are well-suited for the following positions:
  • Data Analyst
  • Data Scientist
  • Analytics Manager/Lead
  • Machine Learning Engineer
  • Statistical Programming Specialist

What are the job responsibilities of Data Scientist?

Role and Responsibilities of a Data Scientist:
  • To begin the discovery process, ask the proper questions.
  • Gather information.
  • Cleanse and process the data.
  • Data should be integrated and stored.
  • Data exploration and exploratory data analysis are the first steps in the process.
  • Pick one or more potential models and algorithms to work with.

What are the tools covered in this Data Science Course?

  • SAS.
  • Apache Spark.
  • BigML.
  • D3.
  • Excel.
  • ggplot2.
  • Tableau.
Show More

Overview of Data Science Training in Bhubaneswar

ACTE Data Science in Bhubaneswar. It is rated as the best data science education institution in Bhubaneswar. We provide state-of-the-art data science training in Bhubaneswar. Additional data science training provided by ACTE provides real-time project experience and guaranteed job placement support. If you are looking for the best data science courses in Bhubaneswar and have the most experienced teachers in the industry, then ACTE is your perfect data science training facility.It is a method of using statistical analysis tools, machine/deep learning and related tools to use data to evaluate systems and solve business problems. It is a combination of different tools, techniques, algorithms and principles of deep learning/machine learning with a single purpose. It is looking for hidden patterns in the original data.

Additional Info

Data Science is a multidisciplinary field that extracts meaningful knowledge and insights from large amounts of structured and unstructured data using scientific inference and mathematical algorithms. These algorithms are implemented through computer programmes, which are typically run on powerful hardware due to the large amount of processing required. Data Science is a field that combines statistical mathematics, machine learning, data analysis and visualisation, domain knowledge, and computer science. The most important component of Data Science, as the name implies, is “Data.” No amount of algorithmic computation can produce meaningful insights from illegitimate data. Data science is concerned with many different types of data, such as image data, text data, video data, time-dependent data, and so on.

History of Data Science

The term "Data Science" has been mentioned in various contexts over the last thirty years, but it is only recently that it has gained international acceptance and recognition. In 2012, Harvard Business Review dubbed it "The Sexiest Job of the Twenty-First Century."

Origin of the Concept

Though it is unclear when and where the concept was originally developed, William S. Cleveland coined the term “Data Science” in 2001. Shortly thereafter, in April 2002 and The International Council for Science: Committee on Data for Science and Technology's publication of the “CODATA Data Science Journal” in January 2003, and Columbia University's publication of the “Journal of Data Science,” respectively, launched the Data Science journey.

Furthermore, it was around this time that the “dot-com” bubble was in full swing, resulting in widespread adoption of the internet and, as a result, the generation of massive amounts of data. This, along with technological advancements that resulted in faster and cheaper computation, was responsible for introducing the concept of “Data Science” to the rest of the world.

Recent Additions to the Field of Data Science :

    Since its inception in the early 2000s, the field of data science has been expanding. With the passage of time, more and more cutting-edge technologies are being integrated into the field. Some of the more recent additions are detailed below.

  • Artificial Intelligence :

    Machine Learning has long been recognised as a key component of Data Science. However, with increased parallel compute capabilities, Deep Learning has been the most recent and one of the most significant additions to the Data Science field.

  • Edge Computing :

    Edge computing is a new concept related to the Internet of Things (Internet of Things). In essence, edge computing brings the Data Science pipeline of information collection, delivery, and processing closer to the source of the information. This is possible with IoT, which was recently added as a component of Data Science.

  • Security :

    In the digital space, security has been a major challenge. Malware injection and the concept of hacking are fairly common, and all digital systems are susceptible to them. Fortunately, there have been a few recent technological advancements that use Data Science techniques to prevent digital system exploitation. For example, when compared to traditional algorithms, Machine Learning techniques have proven to be more capable of detecting computer viruses or malware.

Role of Big Data in Data Science

The term "Big Data" refers to a large collection of heterogeneous structured, semi-structured, or unstructured data. Databases are typically incapable of handling such large datasets.Data, as previously stated, is the most important component of Data Science. As a general rule, “the more data there is, the better the insights.” As a result, Big Data is crucial in the field of Data Science. Big Data is distinguished by its diversity and volume, both of which are critical for Data Science. Data Science is the study of complex patterns in Big Data through the development of Machine Learning models and Algorithms.

Applications of Data Science

    Data Science is a field that can be used to solve complex problems in almost any industry. Every business applies Data Science to a different application in order to solve a different problem. Some businesses rely entirely on Data Science and Machine Learning techniques to solve a specific set of problems that would otherwise be unsolvable. Some of these Data Science applications, as well as the companies behind them, are listed below.

  • Internet Search Results (Google):

    When a user searches for something on Google, complex Machine Learning algorithms determine which of the search results are the most relevant to the search term (s). These algorithms aid in the ranking of pages so that the most relevant information is presented to the user at the click of a button.

  • Recommendation Engine (Spotify):

    Spotify is a music streaming service that is well-known for its ability to recommend music based on the user's preferences. This is an excellent example of Data Science in action. Spotify's algorithms use the data generated by each user over time to learn the user's musical tastes and recommend similar music to him/her in the future. This allows the company to attract more users because Spotify is more convenient for the user because it does not require much attention.

  • Intelligent Digital Assistants (Google Assistant):

    Google Assistant, like other voice or text-based digital assistants (also known as chatbots), is one application of advanced Machine Learning algorithms. These algorithms can convert a person's speech to text (even if it has different accents and languages), understand the context of the text/command, and provide relevant information or perform a desired task simply by speaking to the device.

  • Waymo (Autonomous Driving Vehicle):

    Waymo vehicles are at the cutting edge of technology. Companies such as Waymo use high-resolution cameras and LIDARs to capture live video and 3D maps of their surroundings, which are then fed into Machine Learning algorithms that help the car drive itself. The data in this case consists of the videos and 3D maps captured by the sensors.

  • Spam Filter (Gmail):

    Another important Data Science application that we use in our daily lives is spam filters in our emails. These filters automatically separate spam emails from the rest of the inbox, resulting in a much cleaner email experience for the user. Data Science, like the other applications, is a critical building block in this case.

  • Filter for Abusive and Hate Speech (Facebook):

    Similar to spam filters, Facebook and other social media platforms use Data Science and Machine Learning algorithms to filter out abusive and age-restricted content from the unintended audience.

  • Robotics (Boston Dynamics):

    Machine Learning is a key component of Data Science, and it is what powers the majority of robotics operations. Boston Dynamics, for example, is at the forefront of the robotics industry, developing autonomous robots capable of humanoid movements and actions.

  • Automatic Piracy Detection (YouTube):

    The vast majority of videos uploaded to YouTube are original works created by content creators. However, pirated and copied videos are frequently uploaded to YouTube, which is against their policy. Due to the sheer volume of daily uploads, it is impossible to detect and remove such pirated videos manually. This is where Data Science comes in to help detect and remove pirated videos from the platform.

The Life Cycle of Data Science

    The field of Data Science is not a single step process. It has many steps involved in it. These steps are listed below.

  • Project Analysis:

    This step is more concerned with project management and resource assessment than with direct algorithm implementation. Instead of starting a project blindly, it is critical to determine the project's requirements in terms of the source of data and its availability, the number of human resources available, and whether the budget allocated for the project is adequate to successfully complete it.

  • Data Preparation:

    The raw data is converted to structured data and cleaned in this step. This includes data analysis, cleaning, and dealing with missing values.

  • Exploratory Data Analysis (EDA) :

    This is a critical step in Data Science in which the Data Scientist investigates the data from various perspectives and attempts to draw preliminary conclusions from the data. Data Visualization, Rapid Prototyping, Feature Selection, and Model Selection are all part of this process. In this step, a different set of tools is used. R or Python for scripting and data manipulation, SQL for interacting with databases, and various libraries for data manipulation and visualisation are the most commonly used.

  • Model Building:

    Once the type of model to be used is determined by the EDA, the majority of resources are directed toward developing the model with ideal hyperparameters (modifiable parameters), so that it can perform predictive analysis on similar but previously unseen data. Various Machine Learning techniques, such as Clustering, Regression, Classification, or PCA (Principal Component Analysis), are applied to the data to extract valuable insights.

  • Deployment :

    After the model has been successfully built, it is time to release it from its sandbox into the real world. This is where model deployment comes into play. Until now, all of the steps had been devoted to rapid prototyping. However, once the model has been successfully built and trained, it will be used in the real world, where it will be deployed. This can take the form of a web app, a mobile app, or it can be run in the server's backend to crunch high-frequency data.

  • Real World Testing and Results:

    After the model has been deployed, it is subjected to previously unseen data from the real world in real time. The model may perform admirably in the sandbox but fall short after deployment. This is the phase in which the model output must be constantly monitored in in order to detect scenarios where the model fails If it fails at any point, the development process is not interrupted. return to Step 1 If the model is successful, the key findings are documented and communicated to the stakeholders. in order to detect scenarios where the model fails If it fails at any point, the development process is not interrupted. back to Step 1. If the model succeeds, the key findings are noted and reported to the stakeholders.

What role does Data Science play in relation to the other buzzwords?

Artificial intelligence is referred to by several terms, including AI, Machine Learning, and Deep Learning. The term "Data Science" appears to be a rather enigmatic one, with no clear definition or boundaries. "Artificial Intelligence," "Machine Learning," and "Deep Learning" are buzzwords that are frequently used interchangeably or in conjunction with "Data Science." Let us define each of these terms' parameters.Machine Learning, as previously stated, is a subset of Data Science. Deep Learning, as shown in the diagram below, is a subset of Machine Learning, which is a subset of Artificial Intelligence.

Although Data Science includes elements of Artificial Intelligence, Machine Learning, and Deep Learning, it is much broader than these three subdomains. Data Science encompasses Statistical Programming, Data Analysis, Data Mining, Big Data, and more recent additions such as IoT, Edge Computing, and Security.As a result, Data Science is a complex field of scientific data study that incorporates a substantial portion of some of the most recent advances in Computer Science and Mathematics.

Skills required to become a Data Scientist

    Data Science, as mentioned in the previous section, is a complex field. As a result, mastery of multiple sub-fields is required, which add up to the complete knowledge required to be a Data Scientist.

    1.Applied mathematics:

    The first and most important field of study to become a Data Scientist is mathematics; specifically, probability and statistics, linear algebra, and basic calculus.

  • Statistics:

    Conducting statistical inference on data is critical in EDA and algorithm development. Furthermore, statistics are used as the foundation of the majority of Machine Learning Algorithms.

  • Linear Algebra:

    Working with large amounts of data necessitates the use of high-dimensional matrices and matrix operations. Because the data that the model receives and outputs are in the form of matrices, any operation performed on them employs the fundamentals of Linear Algebra.

  • Calculus:

    Because Data Science includes Deep Learning, calculus is extremely important. Gradient calculation is critical in Deep Learning and is performed at each step of computation in Neural Networks. This necessitates a solid understanding of differential and integral calculus.

  • 2.Algorithmic Knowledge:

    Although Data Science does not typically involve the development and design of Algorithms in the same way that other applications of Computer Science do, it is still necessary for a Data Scientist to have a solid understanding of Algorithms. This is due to the fact that, at the end of the day, Data Scientists are programmers who are expected to create programmes that derive meaningful insights from data. Algorithmic knowledge enables the Data Scientist to write meaningful efficient code, which saves both time and resources and is thus highly valued.

    3.Programming Languages (R and Python):

    While any programming language can be used for any logical use case, including Data Science, the most commonly used languages are R and Python. Both of these languages are open source and thus have a large community support, as well as multiple libraries developed with Data Science in mind that are relatively easy to learn and use. A Data Scientist cannot apply algorithmic or mathematical knowledge to data unless they are familiar with programming languages.

    4.Proper Programming Environment:

    Because solid programming knowledge is one of the most important requirements for Data Science, a convenient platform to write and execute code is required. The IDE, or Integrated Development Environment, is the name given to this platform. There are several IDEs to choose from, some of which have been designed specifically for Data Science. This article discusses the Top 10 Python IDEs.

    5.Machine Learning Frameworks:

    Machine Learning is an important part of Data Science, and its implementation necessitates the use of specific libraries and frameworks, knowledge of which is required of any Data Scientist. Some of the most popular Machine Learning frameworks are listed below.

  • Numpy :

    Numpy is a library that enables the simple implementation of linear algebra and data manipulation. Pandas is a data-loading, data-modification, and data-saving library. This is also used in data manipulation.

  • Matplotlib:

    This is one of the most widely used data visualisation libraries.

  • Seaborn:

    Matplotlib is used to visualise more complex data, and this is a wrapper around it.

  • Sklearn:

    This is where most machine learning algorithms and data preprocessing techniques are applied and implemented. Tensorflow is a deep learning framework supported by Google that allows for the simple implementation of various types of neural networks.

  • PyTorch:

    A deep learning framework that is widely used, similar to Tensorflow.

  • Keras:

    This is a wrapper that works in conjunction with Tensorflow and allows for the relatively simple implementation of Deep Learning techniques.

  • OpenCV:

    This is a computer vision framework and is usually used for Image Processing and image manipulation. This is used for video or image-based data.

  • 6.SQL:

    Databases are extremely important in the field of Data Science because they are the most appropriate method of storing data. A thorough understanding of one or more database technologies such as MySQL, MariaDB, PostgreSQL, MS SQL Server, MongoDB, Oracle NoSQL, and others is also required.

Why businesses need Data Science?

    We've progressed from working with small sets of structured data to large mines of unstructured and semi-structured data coming in from a variety of sources. When it comes to processing this massive pool of unstructured data, traditional Business Intelligence tools fall short. As a result, Data Science includes more advanced tools for working with large volumes of data from various sources such as financial logs, multimedia files, marketing forms, sensors and instruments, and text files.

    The following are relevant use-cases that are also the reasons why Data Science is becoming popular among organisations: Predictive analytics uses data science in a variety of ways. In the case of weather forecasting, information is gathered from a variety of great precision. This helps in taking appropriate measures at the right time and avoid maximum possible damage.

  • Traditional models that drew insights from browsing history, purchase history, and basic demographic factors never produced product recommendations this precise. With data science, vast amounts and types of data can be used to train models better and more effectively, resulting in more precise recommendations.

  • Data Science also aids in making sound decisions. Self-driving or intelligent automobiles are a prime example. An intelligent vehicle collects data from its surroundings in real time using various sensors such as radars, cameras, and lasers to create a visual (map) of its surroundings. It makes critical driving decisions, such as turning, based on this data and an advanced Machine Learning algorithm.

10 Interesting Apps For Data Scientists To Enhance Their Skills

    Finding time to learn a new skill can be challenging, especially in the competitive field of data science. With the rapid increase in mobile phone usage, mobile apps have revolutionised the learning system. Aside from adding excitement to the process, mobile apps allow data science enthusiasts to learn and upskill themselves while on the go.We have shared a few interesting apps in this article that can help data scientists learn, practise, and improve their skills.

  • Data Science 101:

    Data Science 101, as the name implies, is a learning app that can assist users in learning machine learning algorithms. This app serves as a beginner's guide for data science enthusiasts who want to learn and practise data science while building machine learning models. It also serves as a high-quality resource for users to learn about the field and various ML algorithms such as linear regression, KNN, SVM, and so on. The app can also be used to create various data science projects and includes the necessary codes.

  • Elevate :

    Elevate is a brain training mobile app available on both iOS and Android that aims to improve users' cognitive skills such as focus and memory.speaking abilities, processing speed, and math skills. Every day, three exercises are chosen based on the user's previous performance for the personalised training sessions. It is a cognitive training tool that data scientists can use to improve their communication as well as analytical abilities This app has been named Apple's "App of the Year" and has been downloaded more than 25 million times by users. The app includes a 14-day free trial as well as a free version.

  • Lumosity :

    Lumosity, created by Lumos Labs, is yet another customised game and brain training app that has grown in popularity over the years. This free mobile app, which is also available on iOS and Android, is intended to improve memory, increase focus, and test your brain's sharpness. Lumosity is a collection of fun and interactive puzzle games that can assist data scientists in keeping their minds active and applying critical thinking to problem solving. It also aids in the development of logical and mathematical abilities. The app, which has a 4.2 rating on Google Play, translates cognitive science into easily accessible brain training.

  • NeuroNation :

    NeuroNation, which received Google's Best App Prize, is a scientific custom-tailored brain training app designed to improve users' brain activity. It helps data scientists improve their intelligence and logical thinking by providing them with 60 different sets of activities and exercises. This app, which is available for both iOS and Android, allows users to challenge their opponents in games and exercises, as well as keep track of their performance. NeuroNation's 15-minute training session, which claims to change users' lives, can provide new momentum for users' brains.

  • Math Workout :

    Data science is the source of this term. Math has always been essential for data scientists. Math Workout is a free Android app that helps users who have difficulty with numbers. This app allows users to enjoy and solve math problems while also providing Kumon-based brain training challenges. MathWorkout not only improves users' psychological math skills, but it also teaches them how to perform numerical calculations with their fingertips. Aparenthusiast also allows kids to practise basic math and improve their speed or fluency, as well as take on advanced challenges. Although it is a beginner-level application, it does assist users in developing their numerical instincts. Armenian, Chinese, Russian, and Hindi are among the ten languages supported by the app.

  • QPython :

    Python is the most widely used programming language for coding. With the QPython app, it is now possible to learn this programming language from a mobile device. QPython, which is available for Android users, is a Python engine that assists data science enthusiasts in learning more about this language. It includes a Python interpreter, runtime environment, editor, QPYI and SL4A libraries, and is Python 2.7 compatible. This highly rated app on Play Story includes a useful Python library, as well as the ability to execute codes and documents from QR codes.

  • Basic Statistics :

    The Basic Statistics app, which is available for both iOS and Android, is a fun and easy way to learn and revise statistics. Basic Statistics app simplifies complex statistical jargons and concepts for data scientists by providing simple explanations, quizzes, and examples. It is critical for data scientists to have a basic understanding of statistics in order to derive insights from large data sets. This app will help new developers and data scientists improve their understanding of frequency distribution, data description, hypothesis testing, and other topics.

  • Probability Distributions :

    This app, like the Basic Statistics app, aids in the computation of probabilities; however, it is available on both iOS and Android. This app is popular among statistical students and researchers in addition to data scientists. Probability Distributions app, created by Dr. Matthew Bognar of the University of Iowa, computes probabilities and quantiles for the binomial, geometric, Poisson, negative binomial, hypergeometric, normal, t, chi-square, F, gamma, log-normal, and beta distributions.

  • Programming Hub :

    Programming Hub is a free app available on both iOS and Android that provides programming manuals for languages such as Python, C, C++, C#, R programming, and others. With a collection of 5000 programmes, this app is popular among developers because it makes programming fun and interactive for users to learn new skills and enjoy the process. Programming Hub was developed through research and collaboration with Google experts to provide an ideal entry point into the complex world of coding.

  • Learn Python :

    Learn Python, like QPython, is an app that will help data science enthusiasts learn Python on their phones while they are on the go. This app is only available for Android and covers basic tutorials and short lessons on Python, data types, control structures, functional programming, and other topics. These tutorials will assist novices in learning the new language as well as professional data scientists in regularly brushing up on their skills.

Show More

Key Features

ACTE Bhubaneswar 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 Bhubaneswar
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.
Show More
Show Less
Need customized curriculum?

Hands-on Real Time Data Science Projects

Project 1
Breast Cancer Classification Project

The main objective of this manuscript is to report on a research project where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability.

Project 2
Traffic Signs Recognition Project

The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera and given that traffic sign recognition is one of the most challenging problems for driving assistance systems.

Project 3
Weather Prediction Project

Its primary purpose is to bring forecasters closer to the elusive goal of accuracy and opposed to subjective forecasting, an objective system can use only a limited number of parameters as predictors of future events.

Project 4
Wine Quality Analysis Project

To evaluate compositional changes and flavor development during ripening in order to develop better harvest indices, and to develop practical methods to evaluate optimal winegrape maturity.

Our Best Hiring Placement Partners

ACTE Bhubaneswar offers arrangement openings as extra to each understudy/proficient who finished our study hall or internet preparing. A portion of our understudies are working in these organizations recorded underneath.
  • The ACTE preparing offered at State of the art Home goes with 100% guaranteed position help. We have a committed assembling for conditions; they interface with different affiliation HRs and bring in any case various occupation openings for our student as could reasonably be expected. We correspondingly have an other bound together specialist preparing social event for course selected learners.
  • Our position get-together will what's more discover the up-and-comers about the stroll around meeting.
  • ACTE has given position bunches examined the best in the business working predictable in tracking down a reasonable situation opportunity for our learners. With tie ups with over 3000+ relationship across India.
  • We are related with top affiliations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM, etc It make us ready to place our understudies in top MNCs across the globe.
  • After affirmation of 70% Data Science Training in Bhubaneswar instructive class content, we will a mass the get-together calls to understudies and set them up to vis-à-vis joint effort.
  • Our tutors help the learners in building their resume expertly and also support their confirmation by giving critical snippets of data to them about demands questions and managing social events with mock get-together get-togethers.

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.

Get Certified

About Satisfactory Data Science Mentor

  • Our Data Science Training in Bhubaneswar learning coach are furnished experts with more than 9+ huge stretches of reliable power who are for the most part seen as the best in the business and now serve for huge affiliations.
  • Our guide recognizes obligation to give the entire of his ability to the social event to up-expertise reflecting corporate development.
  • We have each course material to fathom which is set up by our assistants and we will present to you in the wake of meeting wraps up.
  • Instructors chipped away at different continuous brought together worker engineer projects and with solid hypothetical and utilitarian information.
  • Our mentors are especially proficient and went with 10+ huge stretches of affiliation. We attempt to enlist Industry experience-based experts just to show our understudies. The aides bring their industry experience into the homeroom and educate from an industry perspective.
  • We are building a social occasion of Incorporated worker fashioner mentors and people for their future assistance and help with subject. Our coach will be founded on supporting placement also.

Data Science Course Reviews

Our ACTE Bhubaneswar 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

Great place to learn Data Science from ACTE in Bhubaneswar. The instructor Ashish is excellent in his knowledge with respect to Data Science. I received a complete transparency in knowledge sharing from the instructor with real time scenarios problem and moreover with practical basic examples. Instructor and Institute faculties are easily accessible to guide us from scratch to the complex doubts.


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.

View More Reviews
Show Less

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
Show More
Request for Class Room & Online Training Quotation

Related Category Courses

data science with sas training acte
Data Science With SAS Training in Chennai

Beginner & Advanced level Classes. Hands-On Learning in Data Science Read more

data science with r training acte
Data Science with R Training in Chennai

Beginner & Advanced level Classes. Hands-On Learning in Data Science Read more

data science with python training acte
Data Science with Python Training in Chennai

Beginner & Advanced level Classes. Hands-On Learning in Data Science Read more

python training acte
Python Training in Chennai

Learning Python will enhance your career in Developing. Accommodate the Read more

r programing training acte
R Programming Training in Chennai

Beginner & Advanced level Classes. Hands-On Learning in R Programming. Read more

machine Learning training acte
Machine Learning Training in Chennai

Live Instructor LED Online Training Learn from Certified Experts Beginner Read more

ai training acte
Artificial Intelligence Training in Chennai

Live Instructor LED Online Training Learn from Certified Experts Advanced Read more