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Data Science Training in Delhi

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  • High-Rated Course With Accelerated Certification.
  • Most beneficial Practice for interview Preparing Methods in Data Science.
  • Continuance Way for Student’s Portal, Study Materials, Videos & Top MNC Interview Question.
  • Moderate Fees with a Great curriculum Planned by Industrial Data Science Expert.
  • Performed by 9+ years of Data Science Certified Specialist.
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Weekdays Regular

08:00 AM & 10:00 AM Batches

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


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  • This course gives you the concepts and practical skills necessary to find your first job in the most dynamic technology industry.
  • The huge amounts of data produced through business interactions can be read, arranged and exploited.
  • This course includes a comprehensive and constantly updated range of technology from the mathematical theory and concepts that support data science, to practical implementations to predict system behaviour.
  • Perfect for B.Tech, B.E, B.Sc. or Any Computer Relevent graduates from 0-3 years' experience and degree.
  • We train students wih complete course knowledge to get a placement in high salary companies.
  • All the materials and live sessions recording will be given for the interview preparations.
  • 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
  • Online Training
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Course Objectives

This is a fantastic course for newcomers. The course begins with an overview of mathematical and statistical principles. Students are taught Python and R, two of the most widely used programming languages in the world. If a newcomer learns the ropes and works on polishing the necessary abilities, he or she can become a data analyst. You may continue learning new ideas related to data analysis by enrolling in massive open online courses (MOOCs), which can help you remain ahead of the competition.
A bachelor's degree in mathematics, statistics, computer science, or data science is required. Any engineering subject with a bachelor's degree is acceptable. You are qualified to enroll in this course if you satisfy these conditions.
You will attend 184 hours of classroom sessions over four months in this hybrid program. For a further three months after completion, you will have access to the online Learning Management System for recorded videos and assignments. The online tasks will take a total of 150 hours to complete. Aside from that, you'll spend a month working on a real project.
  • Introduction to Python and R programming are among the subjects covered in this course.
  • Analyze exploratory data
  • Statistical Inference
  • Distribution of Probabilities
  • Visualization of Data
  • Testing Hypotheses
  • Data Exploration Learning that is supervised
  • Modeling Predictive
For a variety of reasons, the answer is a resounding YES. Digitalization is generating massive amounts of data, and the need for Data Science specialists who can assess and extract relevant insights is growing, resulting in millions of new employment in the field. There is a significant gap between supply and demand, resulting in a large number of job openings and incomes. Data Scientists are regarded as the most valuable employees on the market. Data Scientist careers are long-lasting and fulfilling since data creation is growing by leaps and bounds, and the demand for Data Scientists will continue to grow indefinitely.
Professionals who may consider taking a Data Science course to further their professions include:
  • Any professional with logical, mathematical, and analytical abilities.
  • Experts in the fields of business intelligence, data warehousing, and reporting
  • Statisticians, Economists, and Mathematicians are all types of statisticians.
  • Programmers of software
  • Analysts in business
  • Consultants that specialize in Six Sigma
  • Freshmen from any discipline with strong analytical and logical abilities.
  • Using a range of tools and data analysis approaches, manipulate and analyze complicated, high-volume, high-dimensionality data from many sources.
  • Throughout the development process, translates business needs and provides solutions in compliance with company strategies, standards, and processes.
  • Develop business cases for R&D efforts, and give professional advice on data science use cases and solutions to product managers, developers, architects, and business partners.
  • Using a variety of open source technologies, design highly scalable distributed systems.
  • Brief the staff on high-performance algorithms and Python statistical tools.

What will I learn in this Data Science training?

  • A Data Scientist's responsibilities and roles.
  • An organization's data is tested, assessed, and managed.
  • Breakdown of prediction/forecast and analysis using multiple techniques
  • Techniques for sampling
  • Working with software and systems that make recommendations
  • Using analytics tools and installing them
  • Linear and logistic regression techniques have been used in the past.
  • Clustering is being used for analysis.

What are the Job roles for candidates with knowledge in Data Science?

  • Data Engineer
  • Data Scientist
  • Data Visualizer
  • Data Analyst
  • Business Analyst

What are the important skills you will learn from this Data Science training?

After enrolling in this Data Science course in Bangalore, you will gain the following skills:
  • Learn everything there is to know about data structure and data manipulation.
  • For data analysis, you should be able to recognize and employ linear and non-linear regression models, as well as classification approaches.
  • Learn how to use supervised and unsupervised learning models including linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline to solve problems.
  • Use the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave to perform scientific and technical computing.

What are the prerequisites to learn a data science Course?

Professionals who want to be successful in this Data Science Course should have the following skills:
  • Basic statistical expertise is required.
  • Any programming language requires a basic grasp.

How do I become a Data Scientist?

This Data Science course in Bangalore, designed in collaboration with IBM, will provide you with an understanding of Data Science tools and processes, allowing you to flourish in your future career as a Data Scientist. IBM and Simplilearn will provide you an industry-recognized credential attesting to your new abilities and on-the-job competence. This Data Science Training will teach you how to use R and Python, as well as Machine Learning algorithms, data reprocessing, regression, clustering, SAS data analytics, and Tableau data visualization.
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Overview of Data Science Training in Delhi

A Data Scientist is a researcher who needs to prepare large volumes of large data for analysis, develop and synthesize complicated quantitative algorithms and provide impressive visualizations of the results for senior management. Data science Course in Delhi improves business decisions by speeding up the whole process and improving the direction. An individual who loves to play numbers and figures must be a data scientist. The skill of a data scientist which is most sought after is a strong analytical attitude mixed with solid industrial understanding.

They must be able to communicate the technical concepts to non-technical people in more communication than average Provides data science courses to cover the whole lifecycle principles for data acquisition, data extraction, data cleaning, data exploration, data transformation, feature engineering, data integration, data mining, prediction model building, and customer solution visualized and deployed. Competency and tools covering statistical analysis, text mining, model of regression, testing for hypothesis, predictive analytics, machine learning, deep learning, natural language processing, predictive modeling, predictive modeling, R studio, Tableau, Spark, Hadoop, languages like R programming, python are cover existent programming languages.

Additional Info

Intro of Data Science?

A data scientist analyzes data by combining domain knowledge, programming skills, and mathematical and statistical skills. To produce artificial intelligence (AI), data scientists consult machine learning algorithms on numbers, text, images, videos, audio, and more in order to automate tasks that normally require human intelligence. The analysis and business users of these systems can translate these insights into tangible business value. Organizations today are collecting and creating large volumes of data that can be analyzed and turned into actionable insights through data science. By preparing data for analysis, processing it, performing advanced analysis, and presenting it, data science can identify patterns and assist stakeholders in drawing informed conclusions.

To prepare data for specific types of processing, it may be necessary to clean, aggregate, and manipulate it. It is necessary to develop and use algorithms, analytics, and artificial intelligence models for analysis. It uses software to discover patterns within data to make business predictions based on these patterns. Through scientifically designed tests and experiments, it needs to be validated that these predictions are accurate. Results should be visualized in such a way that makes it possible for anyone to pick up on patterns and understand trends using data visualization tools.

Why is Data Science Important?

Magic is created by data. For industries to make well-informed decisions, they require data. Raw data is converted into meaningful insights through data science. Data science is therefore needed by industries. Data Scientists are wizards who know how to work with data to create magic. Using whatever data he comes across, a skilled Data Scientist will be able to discover meaningful information. As a result, the company is heading in the right direction. Data-driven decisions are a crucial aspect of the company, and he is an expert in this area. An expert in Statistics and Computer Science, the Data Scientist acts as an expert in a number of fields. Business problems are solved with his analytical abilities.

Learn How to Become a Data Scientist :

  • Assigning patterns to data is the task of the Data Scientist. Data Scientists are expert at problem-solving. The purpose of his work is to identify redundant samples and gain insight from them. To extract information from data, data scientists use a range of tools.

  • It is the role of a Data Scientist to manage, store, and analyze structured and unstructured forms of data. Its responsibility is primarily to analyze and manage data, but it also depends on the industry in which the company works. For a Data Scientist to do this, he or she needs to have industry-specific domain knowledge.

  • In the past, we had largely organized and modestly sized data, which were analyzed by simple BI tools. In contrast to traditionally organised data, most data today is unstructured or semistructured. Below is a data trend that suggests that more than 80% of the information will be unstructured.

  • Data is collected from several sources, including financial records, text files, multifunctional forms, sensors, and tools. There is an enormous quantity and diversity of data that cannot be processed by BI tools alone. As a result, we need ever more sophisticated analytical tools and algorithms to process, analyze, and develop insights. Data science is not just popular because of its popularity.

  • Here are a few ways that data science is used in different contexts. Based on current data like navigation history, acquisition history, age, and revenue, what is the best method to understand your clients' specific requirements? All of this happened without any uncertainty. Let's see how predictive analytics can be leveraged through data science.

  • Let's take the case of weather predictions. Models can be constructed by collecting and analyzing data from ships, planes, radars, and satellites. As well as anticipating weather, these models aid in preventing natural disasters. Your actions will be informed in advance, helping you to make the right decision.

Data Scientist Role and Responsibilities :

As part of their work, data scientists work closely with business stakeholders to determine how they can use data to achieve their goals. To extract the data the business needs, they develop algorithms and predictive models, analyze the data and share insights with peers. Data is generally gathered and analyzed as follows, even though every project is different :

  • Ask the right questions before beginning the discovery process
  • Acquire data
  • Process and clean the data
  • Integrate and store data
  • Investigation of initial data and exploration of exploratory data
  • Select at least one model and algorithm to use
  • Utilize data science techniques, such as machine learning, modelling, and artificial intelligence
  • Measure and improve results
  • Present final result to stakeholders
  • Make adjustments based on feedback
  • Repeat the process to solve a new problem

Advantages of Data Science :

The various benefits of Data Science are as follows:

1. It’s in Demand :

There is a greatest demand for data science Specialists. There are numerous opportunities available to job seekers. Linkedin predicts it will create 11.5 million jobs by 2020. It is the fastest growing job on the network. Because of this, Data Science is an extremely employable profession.

2. Abundance of Positions :

Data Scientists require a very specific skill-set. Few people possess all of these skills. In comparison with other IT sectors, Data Science is therefore less saturated.

The field of Data Science is therefore very diverse and has many opportunities. While Data Scientists are in high demand, there are not enough of them to meet that demand.

3. A Highly Paid Career :

In the field of data science, you can earn a lot of money. Approximately 116,100 dollars a year is the average salary for Data Scientists, according to Glassdoor. The lucrative nature of Data Science makes it a highly attractive career choice.

4. Data Science is Versatile :

Data Science has a wide range of applications. In the health-care, banking, and consultancy services industries, and in e-commerce, the technology is widely used. Data Science has many applications. Therefore, you will be able to work in a variety of fields.

5. Data Science Makes Data Better :

In order to analyze and process their data, companies require the skills of Data Scientists. Furthermore, they improve the quality of the data as well. Therefore, Data Science involves enriching data and enhancing it for the benefit of the organization.

6. Data Scientists are Highly Prestigious :

Businesses can make smarter business decisions thanks to Data Scientists. Clients benefit from the expertise of Data Scientists. Companies rely on their expertise to improve results. Data Scientists thus hold a key position within an organization.

7. No More Boring Tasks :

The use of data science in various industries has helped automate redundant tasks. Historical data is being used by companies to train machines to perform repetitive jobs. The arduous jobs previously undertaken by humans have been simplified.

8. Data Science Makes Products Smarter :

Utilizing Data Science, industries are able to tailor better products for their customers thanks to Machine Learning. By analyzing users' past purchases, recommendation systems used by e-commerce websites provide personalized insights. As a result, computers can now understand human behavioral patterns and take data-driven decisions.

9. Data Science can Save Lives :

Data Science has contributed greatly to the improvement of the Healthcare sector. Machine learning has made early detection of tumors easier. In addition, many other industries in the health care sector use Data Science to help their clients.

10. Data Science Can Make You A Better Person :

In addition to providing you with a rewarding career, Data Science can also assist you in developing yourself personally. Having a problem-solving attitude will enable you to achieve success. Taking on a Data Science role allows you to combine the best of both worlds, as many Data Science roles bridge IT and Management.

Data scientist skills :

The top five skills for data scientists include a mix of hard and soft skills:

  • Programming :

    This is the "most fundamental of all data scientist skills." You can perform data analyses on large datasets using programming, as well as create your own tools by using the language.

  • Quantitative analysis:

    As a crucial part of analyzing large datasets, that quantitative analysis is an essential skill for running experimental analyses, scaling your data strategy, and implementing machine learning.

  • Product intuition:

    Quantitative analysis will be easier if you understand products. In addition to improving your debugging skills, it will also help you predict system behavior.

  • Communication:

    Strong communication skills are perhaps the most important soft skills across every industry. They will Support you to "leverage all of the previous skills set listed".

  • Teamwork:

    A successful data science career requires teamwork, as well as communication. It calls for selflessness, embracing feedback, and sharing knowledge with your team.

The Data Science job profiles include :

1. Data Scientist :

Several fields are covered by a data scientist. The data scientist might define the problem description and project objectives based on the business objectives. They employ artificial intelligence, train machines to uncover models and trends and make data-based predictions. An artificial intelligence, machine learning, statistics, and data engineering foundation is required.

2. Data Analyst :

It is often the role of the data analyst to collaborate with management and the company to identify project objectives. In this way, data can be gathered and explored in an appropriate manner. Analyze patterns and trends by converting data into patterns. Also, the team is shown the designs and the data based on which the products can be created.

In addition to excellent interpersonal skills, it requires expertise in programming, databases, data analysis, and tools for data visualization. Our main objective will be to master machine learning and cloud platforms such as Azure, IBM, and Google

3. Data Engineer :

Database administrators are traditionally hired and responsible for managing data in organizations. Their duties include maintaining the integrity and availability of the business' databases and ensuring data security. In addition to knowing how to use a classic relational database, disaster recovery and backup methods, and reporting tools, they must be proficient in classic relational databases.

Data engineers develop, maintain, and support scalable data pipelines, and they build APIs to access data repositories. Models of data have become more diversified in their types and expertise, as it relates to data formats and big data technology.

4. Enterprise Data Architect :

At the strategic level, data architects and data managers ensure the quality, accessibility, and security of enterprise data. Data architects in the company design and build pipelines, repositories, and data management facilities.

A database administrator creates and manages an organisation's database by defining technological layers, performance requirements, and database size. A data engineer and manager also collaborate to guarantee data security, data protection, and performance.

Certification course on data science :

It refers to a concept of "combining statistics with data analysis techniques" for "understanding and analyzing real events with data." The Data Science education is based on mathematics, statistics, information science, and computer science and incorporates techniques and ideas from multiple subjects, including machine learning, classification, cluster analysis, information mining, databases, and visualization. You can learn about the data science life cycle and analyze and view different data sets, as well as various machine learning algorithms like K-Means clustering and decision making in the Data Science certification course.

What are the goals of our online course in data sciences?

During the training program in data sciences, professionals will teach you how to become a Certified Data Scientist. Here are the courses in Data Science :

  • Algorithms for machine learning and data science life cycle knowledge
  • Knowledge of several tools and methods for data transformation
  • A comprehensive understanding of data visualisation and optimisation techniques; the ability to perform text and sentiments analysis
  • The exhibition of numerous industrial real-life projects in RStudio
  • A wide array of projects in areas such as health, social media, aviation, and human resources
  • Data science training by a SME to better understand industry standards and best practices
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Key Features

ACTE Delhi 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 Delhi
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
Speech Emotion Recognition Project

The main objectives of the project is to build a model to recognize emotion from speech using the librosa and sklearn libraries and the RAVDESS dataset.

Project 2
Gender and Age Detection with Data Science Project

To build a gender and age detector that can approximately guess the gender and age of the person (face) in a picture using Deep Learning on the Adience dataset.

Project 3
Wallmart Sales Data Set

The objective of the project is to build an application that predict the sales using the Walmart dataset and will help in providing us with the data on future sales.

Project 4
Flipkart Classification Dataset

The main objectives of this project is to forecast sales for each department and increasing labelled dataset using semi-supervised classification.

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  • With a strong highlight on training and errands, our work focused course of action informative classes are especially coordinated, subject-unequivocal, and work focused.
  • Learners will be given fitting mock appraisals and mock gathering dates when they have finished the informative courses.
  • Our ACTE strong relationship in the business, we regularly set up to a great extent grounds position for IT confident to help them with handling their first position or move up their calling ladder.
  • ACTE has submitted gathering of plan which places understudy in top MNC's. We gives constant experience to students and set them up for the current work environment market. Each and every getting ready module and arranged by current industry demand with the objective that less greasy can be expertly and indeed strong.

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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.

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About Satisfactory Data Science Mentor

  • Our Data Science Training in Delhi. The AWS mentors that we have at our foundation are respectful and brief at giving any help. They promptly answer any inquiries or questions that our competitors may have.
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    • 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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      More Than 35% Of Developers Prefer Data Science. Data Science Is The Most Popular And In-Demand Programming Language In The Tech World.

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