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Data Science Course Online Training

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  • Beginner & Advanced level Classes.
  • Hands-On Learning in Data Science.
  • Best Practice for interview Preparation Techniques in Data Science.
  • Lifetime Access for Student’s Portal, Study Materials, Videos & Top MNC Interview Question.
  • Affordable Fees with Best curriculum Designed by Industrial Data Science Expert.
  • Delivered by 9+ years of Data Science Certified Expert | 12402+ Students Trained & 350+ Recruiting Clients.
  • Next Data Science Batch to Begin this week – Enroll Your Name Now!

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03- Apr - 2023

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05- Apr - 2023

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08- Apr - 2023

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08- Apr - 2023

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Online Courses by Certified Experts

Learn From Experts, Practice On Projects & Get Placed in IT Company

  • We train students for interviews and Offer Placements in corporate companies.
  • Ideal for graduates with 0 – 3 years of experience & degrees in B. Tech, B.E and B.Sc. IT Or Any Computer Relevent.
  • You will not only gain knowledge of Data Science and Advance tools, but also gain exposure to Industry best practices, Aptitude & SoftSkills.
  • Experienced Trainers and Lab Facility.
  • IBM Data Science Professional Certificate Guidance Support with Exam Dumps.
  • For Corporate, we act as one stop recruiting partner.We provide right skilled candidates who are productive right from day one.
  • Resume & Interviews Preparation Support.
  • Concepts: Data Science, significance of Data Science in today’s digitally-driven world, components of the Data Science lifecycle, big data and Hadoop, Machine Learning and Deep Learning, R programming and R Studio, Data Exploration, Data Manipulation, Data Visualization, Logistic Regression, Decision Trees & Random Forest, Unsupervised learning, Association Rule Mining & Recommendation Engine, Time Series Analysis, Support Vector Machine - (SVM), Naïve Bayes, Text Mining, Case Study.
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  • One To One Training
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This is How ACTE Students Prepare for Better Jobs


Course Objectives

Data Science is not necessarily hard to learn, but it is a complex field that requires a wide range of skills. It involves an understanding of statistics, programming, machine learning, and data visualization. It also requires an understanding of the underlying business problem and an ability to interpret the data to provide meaningful insights. For those willing to put in the effort to learn the necessary skills, Data Science can be a rewarding and lucrative career.

  • 1. Start with the basics: Get familiar with the fundamentals of data science, including the different types of data, basic data analysis and visualization tools, and the different types of machine learning algorithms.
  • 2. Take an online course: There are many online courses available to help you get started with data science. Look for courses that cover the basics of data analysis, data visualization, and machine learning algorithms.
  • 3. Practice coding: Once you have a basic understanding of data science, start practicing your coding skills. Learn a programming language like Python or R, and start coding small data science projects.
  • 4. Get hands-on experience: Participate in hackathons and data science competitions to get hands-on experience and gain insights into real-world data science problems.
  • 5. Network: Attend local data science meetups and conferences to meet other data scientists and build your network.

ACTE offers a variety of courses related to Data Science, such as Data Science and Analytics, Machine Learning, Python for Data Science, Deep Learning, and Big Data Analytics.

  • 1. High Salary: Data Science Web Developers are highly sought-after and can expect to earn a higher salary than many other web developers.
  • 2. Career Growth: As the demand for data science web developers continues to grow, so does the potential for career growth. This can lead to more job opportunities and higher salaries.
  • 3. Variety: Data Science Web Developers can work on a wide variety of projects and applications, giving them the chance to work with different technologies and platforms.
  • 4. Technical Skills: Data Science Web Developers need to be highly technically proficient in order to effectively use the tools and techniques necessary for their job.
  • 5. Problem-Solving: Data Science Web Developers must be creative and analytical in order to come up with creative solutions to complex problems.
  • 6. Networking: Data Science Web Developers have the opportunity to connect with other professionals in the industry, making it easier to get new job opportunities.

Data Science developer salaries vary greatly depending on experience, location, and other factors. Generally, entry-level Data Science developer salaries range from Rs 5,00,000 to RS 8,00,000 per year, while more experienced developers can expect to make Rs 10,00,000 or more.

The top hiring companies for Data Science developers include Amazon, Microsoft, IBM, Google, Uber, Apple, Oracle, Accenture, Intel, LinkedIn, and Facebook. These companies offer excellent salaries and benefits, as well as exciting opportunities to work with cutting-edge technologies and data sets. With their expansive resources and teams of experienced data scientists, these companies provide an ideal environment for data scientists to learn and grow in their careers.

  • 1. Becoming Familiar with Data Science Tools: Learning the most popular data science tools such as Python, R, SQL, and Tableau, and understanding how to use them to analyze data.
  • 2. Mastering Data Science Concepts: Learning and understanding the concepts of data mining, machine learning, predictive analytics, and data visualization.
  • 3. Developing Data Science Skills: Practicing data manipulation, data cleaning, data analysis, data visualization, and model building.
  • 4. Creating Data Science Solutions: Developing and testing data-driven solutions for business problems.
  • 5. Building & Deploying Models: Building and deploying predictive models to generate actionable insights.
  • 6. Dealing with Big Data: Working with large datasets and mastering big data technologies such as Hadoop and Spark.
  • 7. Explaining Your Work: Being able to explain the results of your analysis and how it relates to business objectives.

What requirements do I need for Data Science Training?

  • A computer with an internet connection
  • A basic understanding of programming and/or statistical analysis
  • A desire to learn and explore data science
  • Access to data sets and/or knowledge of where to find them
  • A text editor or integrated development environment (IDE) such as Sublime Text, RStudio, or Jupyter Notebook
  • Software such as Python, R, or SQL
  • A statistical software package such as SAS, SPSS, or MATLAB
  • A library of data science and machine learning algorithms
  • A data visualization library such as matplotlib or ggplot2
  • Knowledge of how to use tools like GitHub or other version control systems

How do I practice Data Science?

  • Learn programming languages: Start by learning a programming language such as Python or R, which are the most commonly used for data science.
  • Get familiar with Data Analysis Tools: Make sure you are familiar with tools such as SQL and Excel that are commonly used for data analysis.
  • Learn Statistics: Learn basic statistics and the fundamentals of machine learning.
  • Get data: Start finding datasets online that you can use to practice data science.
  • Develop a project: Pick a project to work on that will help you practice data science and apply the skills you’ve learned.
  • Practice: Take the time to practice and hone your data science skills.
  • Share your work: Share your work with others to get feedback and see how others have solved similar problems.

Do You Need Any Special Qualifications to Enroll in this Data Science Training?

No, you do not need any special qualifications to enroll in this Data Science Training. However, an understanding of basic mathematics, statistics, and programming concepts is recommended.

How can you get assistance for preparing a Data Science Training Interview?

The best way to get assistance for preparing for a Data Science Training Interview is to consult with a qualified professional in the field. They can provide valuable advice on how to answer questions, provide tips for staying organized, and offer insights into the types of questions that employers might ask. Additionally, there are a number of online resources available, such as tutorials and practice tests, to help you become more knowledgeable and confident in your data science skills.

Top 5 skill set that you will learn in Data Science?

  • Data Wrangling: This includes the ability to access, manipulate, and prepare data for analysis.
  • Data Visualization: The ability to create visual representations of data to identify patterns and correlations.
  • Statistical Analysis: The ability to apply statistical methods and techniques to data sets to identify meaningful patterns and relationships.
  • Machine Learning: The ability to develop algorithms and models that can identify patterns and make predictions from data.
  • Predictive Analytics: The ability to create predictive models from data to identify trends and forecast outcomes.
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About Data Science Online Training Course

Our ACTE Provides a Data Science Online Course with Professional Training, Which is an interdisciplinary field that combines statistical analysis, machine learning, and programming to extract insights from data. In a data science course, students learn how to collect, clean, and analyze large datasets using tools such as Data Science, R, SQL, and Tableau. They also learn how to build and evaluate predictive models, perform exploratory data analysis, and communicate insights through data visualization. The curriculum typically covers topics such as data wrangling, data visualization, statistical inference, machine learning algorithms, natural language processing, and deep learning. Students may also learn about data ethics, privacy, and security, as well as how to work with big data frameworks such as Hadoop and Spark. Data science courses may be offered at both the undergraduate and graduate levels, and they can be found in a variety of fields, including computer science, statistics, business, and engineering. Graduates of data science programs are in high demand in industries such as finance, healthcare, marketing, and technology, where their skills are needed to make data-driven decisions and improve business performance.

Additional Information

Trending Features of Data Science:

Data science is an ever-evolving field, with new concepts and technologies emerging all the time. Some of the most popular and trending concepts in data science include artificial intelligence (AI), machine learning (ML), deep learning, natural language processing (NLP), computer vision, predictive analytics, and big data. AI and ML are especially popular, as these technologies allow for more sophisticated data analysis and automation of many processes. Deep learning is a subset of machine learning that focuses on algorithms that learn from data and can make decisions or predictions on their own. NLP is a form of AI that enables computers to understand and process natural language, allowing them to interact with humans. Computer vision is the field of computer science dedicated to analyzing and understanding digital images, while predictive analytics uses data-driven techniques to anticipate future trends and behaviors. Big data is a term used to describe large and complex data sets that require special techniques, like AI and ML, to analyze and draw useful insights from.

As a language model, I can provide you with some trending concepts in the field of Data Science:
  • Machine Learning: Machine Learning (ML) is a subfield of artificial intelligence (AI) that allows systems to learn from data, identify patterns and make decisions with minimal human intervention.
  • Deep Learning: Deep Learning is a subset of Machine Learning that uses deep neural networks to learn and solve complex problems such as image and speech recognition.
  • Natural Language Processing: Natural Language Processing (NLP) is a field of study that focuses on the interaction between human language and computers. NLP is used to enable machines to understand, interpret, and generate human language.
  • Big Data: Big Data refers to the large volume of data that is generated every day from various sources such as social media, sensors, and online transactions. Big Data is often analyzed using advanced technologies like Hadoop and Spark.
  • Data Visualization: Data Visualization is the process of converting complex data into visual representations such as charts, graphs, and maps to make it easier to understand and analyze.
  • Artificial Intelligence: Artificial Intelligence (AI) is a broad field that involves creating intelligent machines that can perform tasks that typically require human intelligence, such as reasoning, problem-solving, and decision-making.
  • Internet of Things: The Internet of Things (IoT) is a network of physical devices, vehicles, and other objects embedded with sensors, software, and network connectivity that enables these objects to collect and exchange data.
  • Cloud Computing: Cloud Computing is the delivery of computing services, including servers, storage, databases, networking, software, analytics, and intelligence, over the Internet, which enables users to access resources from anywhere and on any device.
  • Data Mining: Data Mining is the process of discovering patterns and knowledge from large datasets. It is often used in combination with Machine Learning to identify hidden patterns and insights from data.
  • Predictive Analytics: Predictive Analytics involves using statistical techniques and Machine Learning algorithms to analyze data and make predictions about future events. It is used in a variety of applications such as fraud detection, customer segmentation, and risk assessment

What are the Prerequisites for Data Science?

Data science is a rapidly growing field that has a wide range of prerequisites. To become a successful data scientist, it is important to have a strong foundation in data analysis, mathematics, and computer science. A background in statistics is also a must, as is knowledge of machine learning algorithms and techniques. Knowledge of programming languages such as Data Science, R, and SQL is also essential. Additionally, having a deep understanding of databases and the ability to build data pipelines is essential for success in data science. Lastly, having an understanding of data visualization and storytelling is important to effectively communicate insight from data.

The prerequisites for data science can vary depending on the specific job or project requirements, but some general skills and knowledge that are typically required include:

  • Programming Skills: Data scientists should have a strong foundation in programming, especially in languages such as Data Science, R, SQL, and Java.
  • Statistics and Mathematics: A solid understanding of statistics, probability, linear algebra, calculus, and other mathematical concepts is crucial for data scientists to be able to analyze and interpret data accurately.
  • Data Visualization: Data scientists should be proficient in data visualization tools and techniques such as Tableau, Power BI, or ggplot to create clear and understandable visualizations of complex data.
  • Machine Learning: Understanding machine learning concepts such as supervised and unsupervised learning, regression, clustering, and deep learning, is important for developing predictive models.
  • Data Manipulation and Preparation: Data scientists should be able to work with large datasets and know how to clean and preprocess data to prepare it for analysis.
  • Domain Knowledge: A good understanding of the domain or industry in which they are working is essential for data scientists to ask relevant questions and solve real-world problems.
  • Communication Skills: Data scientists should be able to communicate effectively with both technical and non-technical stakeholders, including presenting findings and recommendations in a clear and understandable manner.
  • Continuous Learning: Data science is a rapidly evolving field, and it's essential for data scientists to stay updated with the latest techniques, tools, and technologies to remain competitive.

In summary, data science requires a combination of technical, mathematical, and communication skills, as well as a strong interest in continuous learning and staying up-to-date with the latest trends and technologies.

What are the Skills Required to Become a Data Scientist?

To become a data scientist, several skills are required. First and foremost, a data scientist must have a strong foundation in mathematics and statistics. This includes knowledge of calculus, linear algebra, probability, and statistical inference. In addition, a data scientist must have programming skills and be proficient in languages like Data Science or R. They should also have experience working with databases and be able to extract, clean, and manipulate large datasets. Another essential skill for a data scientist is the ability to visualize data effectively. This involves creating charts, graphs, and other visual representations of data to communicate insights to stakeholders.

A data scientist must also possess strong problem-solving skills and be able to develop and implement models to address complex business problems. Data scientists must have excellent communication skills to explain their findings and insights to non-technical stakeholders. They should be able to translate complex technical concepts into language that can be easily understood by others. Data scientists should possess a strong understanding of machine learning algorithms and be able to apply them effectively to real-world problems. This includes knowledge of deep learning, natural language processing, and computer vision.

  • Programming Skills: Data Science, R, SQL, Java, C/C++, etc.
  • Data Analysis & Visualization: Ability to interpret data and create meaningful visualizations.
  • Machine Learning & Artificial Intelligence: Knowledge of algorithms, models, and techniques for machine learning.
  • Business & Communication Skills: Ability to communicate data-driven insights to stakeholders.
  • Statistical Knowledge: Understanding of basic statistical concepts and methods.
  • Domain Knowledge: Understanding of the industry and data in the field.
  • Data Wrangling & Cleaning: Ability to process and clean data for analysis.
  • Data Mining: Ability to use data mining tools and techniques to uncover insights.
  • Data Storage & Management: Knowledge of databases and data storage systems.
  • Computing & Infrastructure: Knowledge of cloud computing and related technologies.

Overall, becoming a data scientist requires a combination of technical and soft skills. It is essential to continually update and improve these skills to stay current with the rapidly evolving field of data science.

Which are Suitable Job Roles for Data Science Course Completion Persons?

Completion of a data science course opens up many job opportunities for individuals who have a passion for data analysis, modeling, and interpretation. Here are some of the most suitable job roles for data science course completion persons:

1. Data Analyst - A data analyst interprets and analyzes data to provide insights and make informed decisions. They work with large data sets, perform statistical analyses, and communicate their findings to stakeholders.

2. Data Scientist - A data scientist is responsible for analyzing and interpreting complex data to solve business problems. They use machine learning, data mining, and other analytical techniques to develop predictive models and algorithms.

3. Business Analyst - A business analyst works closely with stakeholders to identify business needs and provide data-driven insights. They use data visualization tools to communicate their findings and help drive business decisions.

4. Machine Learning Engineer - A machine learning engineer designs and builds machine learning algorithms and models that enable systems to learn and improve over time. They are responsible for developing algorithms, deploying models, and monitoring performance.

5. Data Engineer - A data engineer designs, develops and maintains the infrastructure required for data storage, retrieval, and analysis. They are responsible for building data pipelines, data warehousing, and ETL processes.

6. Quantitative Analyst - A quantitative analyst uses statistical and mathematical techniques to analyze financial and market data. They use their analysis to develop models that inform investment strategies.

7. Data Architect - A data architect is responsible for designing and maintaining the overall data architecture of an organization. They create data models, design data storage systems, and oversee data migration and integration processes.

8. Data Visualization Specialist - A data visualization specialist creates visual representations of data to help stakeholders better understand and interpret complex data sets. They use tools like Tableau, PowerBI, or Excel to design dashboards and reports.

9. Research Scientist - A research scientist designs and conducts research studies that involve the collection and analysis of data. They use their findings to develop theories and insights that inform future research.

These are just a few examples of the many job roles available to individuals who have completed a data science course. The demand for skilled data professionals is growing rapidly, and there are many opportunities for individuals to build successful careers in this field.

What are the benefits of the DataScience Course?

Data science is a rapidly growing field that combines statistical and computational techniques to analyze and interpret large, complex datasets. Improved job prospects, As more and more companies rely on data to make informed business decisions, the demand for data scientists continues to grow. By taking a data science course, you can gain the skills and knowledge needed to enter this exciting field and increase your job prospects. Enhanced my analytical skills, Data science courses teach students how to collect, clean, and analyze large datasets using statistical and computational methods. These skills are not only valuable in data science but also in many other fields, including finance, marketing, and healthcare.

The ability to make better decisions, Data science courses teach students how to interpret data and use it to make informed decisions. This is particularly valuable in fields such as business and finance, where decisions based on data can have a significant impact on the success of a company or organization.

  • Develop an understanding of data exploration, analysis, visualization, and machine learning techniques.
  • Learn to use the most popular data science tools, such as Data Science, R, Excel, and Tableau.
  • Develop a portfolio of data science projects to show employers.
  • Improve problem-solving and decision-making skills.
  • Acquire the skills to process large amounts of data quickly and accurately.
  • Learn to work with unstructured and structured data.
  • Develop the skills to create data-driven models and predictive analytics.
  • Gain a strong understanding of the fundamentals of data science.
  • Learn to communicate data-driven insights to stakeholders.
  • Become proficient in machine learning algorithms.

Future Scope of DataScience:

The future of data science is incredibly bright, with new technologies and applications constantly emerging. Data science has already transformed many industries, from healthcare and finance to marketing and entertainment, and it is poised to continue to do so in the coming years.

One major trend in data science is the increasing use of artificial intelligence and machine learning. These technologies allow data scientists to build predictive models that can analyze vast amounts of data and make accurate predictions about future outcomes. This has huge implications for fields like healthcare, where predictive models can be used to identify patients who are at risk of developing certain conditions and intervene before they become serious. Another trend is the growing importance of data ethics and privacy.

Data science is likely to continue to play a major role in the development of new technologies and innovations. From self-driving cars to personalized medicine, data science is at the heart of many cutting-edge technologies that are shaping the future. The future of data science is incredibly promising, and there is no doubt that it will continue to transform our world in ways we can't even imagine yet. As the field continues to evolve, it will be exciting to see what new breakthroughs and applications emerge.

What Did We expect in the Salary For DataScience Course Completion?

The salary expectations for someone who completes a data science course can vary widely depending on a number of factors, including the level of education and experience, the specific skills and expertise they have developed, and the industry they work in.

According to salary surveys conducted by various sources, a data scientist with a few years of experience can expect to earn an average annual salary of around Rs 4,00,000 to Rs 15,00,000 in India, depending on the location. However, some data scientists with more experience or expertise in specific areas can earn significantly higher salaries, especially in certain industries such as finance, healthcare, or technology.

It's worth noting that the demand for data scientists is high and growing rapidly, so salaries are likely to continue to increase in the coming years. Additionally, there is significant variation in salaries for data scientists in different countries, with some regions offering higher compensation than others. It's important to keep in mind that salary expectations should not be the only factor considered when pursuing a career in data science. The field can be highly rewarding and intellectually stimulating, and there are many opportunities to make a meaningful impact in a wide range of industries. Ultimately, the decision to pursue a career in data science should be based on a combination of factors, including personal interests, skills, and goals, as well as the potential for financial rewards.

Online Classes:

In ACTE, Enrolling in online classes for a data science course can offer numerous benefits to learners. Online classes offer greater flexibility and convenience as learners can access the course material and attend classes from anywhere and at any time, as long as they have an internet connection. This means that learners can study at their own pace and fit their learning around other commitments such as work or family. Online classes often provide access to a broader range of course offerings and instructors from different geographical locations, giving learners the opportunity to learn from the best in the field. Online classes offer interactive and collaborative learning experiences through features such as discussion forums, group projects, and online chat rooms. These features enable learners to engage with their peers, share ideas, and learn from each other's experiences. Online classes can often be more cost-effective than traditional in-person classes, as they eliminate the need for learners to travel and stay on campus, which can result in significant cost savings.

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Key Features

ACTE 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 Online Training Course
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
Wallmart Sales Data Set

Retail is another industry that extensively uses analytics to optimize business processes.

Project 2
Flipkart Classification Dataset

This project is to forecast sales for each department and increasing labelled dataset using semi-supervised classification.

Our Top Hiring Partner for Placements

ACTE offers placement opportunities as add-on to every student / professional who completed our classroom or online training. Some of our students are working in these companies listed below.
  • We are associated with top organizations like HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc. It make us capable to place our students in top MNCs across the globe
  • We have separate student’s portals for placement, here you will get all the interview schedules and we notify you through Emails.
  • After completion of 70% Data Science training course content, we will arrange the interview calls to students & prepare them to F2F interaction
  • Data Science Trainers assist students in developing their resume matching the current industry needs
  • We have a dedicated Placement support team wing that assist students in securing placement according to their requirements
  • We will schedule Mock Exams and Mock Interviews to find out the GAP in Candidate Knowledge

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.

  • 1. Certified Analytics Professional (CAP)
  • 2. Microsoft Certified Solutions Expert (MCSE): Data Management and Analytics
  • 3. IBM Certified Data Scientist
  • 4. Cloudera Certified Professional (CCP): Data Scientist
  • 5. SAS Certified Data Scientist
  • 6. Cloudera Certified Associate (CCA) Data Analyst
  • 7. Tableau Desktop Qualified Associate
  • 8. Oracle Certified Professional (OCP): Data Scientist
  • 9. Amazon Web Services (AWS) Certified Data Analytics – Specialty
  • 10. Hortonworks Certified Associate (HCA) – Data Analyst

Yes, there are several certifications that are beneficial for those looking to pursue a career in data science. These include the Certified Analytics Professional (CAP), the Certified Data Scientist (CDS), the Certified Data Professional (CDP), and the Certified Business Intelligence Professional (CBIP). Specialized certifications such as the Oracle Certified Professional Data Scientist credential or the Cloudera Certified Professional Data Engineer certification can be helpful for those looking to specialize in a certain area.

  • 1. Education: Most data science certifications require applicants to have a minimum of a bachelor’s degree in a related field such as computer science, mathematics, or statistics.
  • 2. Experience: Most certifications also require some amount of experience in data science or a related field.
  • 3. Technical Skills: Data science certifications often require applicants to demonstrate a certain level of proficiency in programming languages, data analysis and statistical software, databases, and machine learning algorithms.
  • 4. Professional Development: Most data science certifications also require applicants to have completed some type of professional development, such as attending conferences or taking courses.
  • 5. Exam: Applicants are typically required to take and pass an exam in order to obtain certification.

The amount of time it takes to complete a data science certification varies depending on the program, but generally, a full certification program can take anywhere from six months to two years depending on the program and the student's commitment.

With a data science certification, you could be eligible for a variety of jobs, including data engineer, data analyst, data scientist, data architect, machine learning engineer, big data engineer, business intelligence analyst, artificial intelligence engineer, and more.

Complete Your C ourse

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 Experienced Data Science Trainer

  • Our Data Science Training . Trainers are certified professionals with 7+ years of experience in their respective domain as well as they are currently working with Top MNCs.
  • As all Trainers are Data Science domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
  • All our Trainers are working with companies such as Cognizant, Dell, Infosys, IBM, L&T InfoTech, TCS, HCL Technologies, etc.
  • Trainers are also help candidates to get placed in their respective company by Employee Referral / Internal Hiring process.
  • Our trainers are industry-experts and subject specialists who have mastered on running applications providing Best Data Science training to the students.
  • We have received various prestigious awards for Data Science Training from recognized IT organizations.

Data Science Course Reviews

Our ACTE 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

My personal experience is very good with with tutors and support staffs, they are very helpful throughout the the learning Data Science Online Course and other aspects. Growth of every student is there motive, thnak you ACTE


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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