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Data Science Certification Training in Abu Dhabi

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

Fee INR 18000

INR 14000

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Training

  • Case Studies and Projects 8+

  • Hours of Training 45+

  • Placement Assurance 100%

  • Expert Support 24/7

  • Support & Access Lifetime

  • Certification Yes

  • Skill Level All

  • Language All

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 Certification and Advance tools, but also gain exposure to Industry best practices, Aptitude & SoftSkills.
  • Experienced Trainers and Lab Facility.
  • IBM Data Science Certification 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 Certification , significance of Data Science Certification in today’s digitally-driven world, components of the Data Science Certification 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.
  • START YOUR CAREER WITH Data Science Certification COURSE THAT GETS YOU A JOB OF UPTO 5 LACS IN JUST 60 DAYS!
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  • One To One Training
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  • Non-IT to IT (Career Transition) 2371+
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  • CTC Greater than 5 LPA4542+
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  • Career Break / Gap Students2588+
09-Dec-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

11-Dec-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

14-Dec-2024
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

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

15-Dec-2024
Sat,Sun

Weekend Fasttrack

(09:00 AM - 02:00 PM)

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

    Hear it from our Graduate

    Course Objectives

    The potential for quantum computing and data science is big within the future. Machine Learning may also method the data abundant quicker with its accelerated learning and advanced capabilities. supported this, the time needed for finding advanced issues is considerably reduced. The role of the data scientist is currently a buzzworthy career. It's standing within the marketplace and provides opportunities for folks that study data science to create valuable contributions to their firms and societies at giant.

    Big data. Data scientists shrewdness to use their skills in maths, statistics, programming, and alternative connected subjects to prepare giant information sets. Then, they apply their knowledge to uncover solutions hidden within the information require on business challenges and goals. Data science is high in demand and explains however digital information is remodeling businesses and serving to them create chiseler and significant selections. therefore digital data is everywhere for folks that are trying to figure as a data scientist.

    • Get comfy with Python
    • Learn information analysis, manipulation, and visualization with pandas
    • Learn machine learning with scikit-learn
    • Understand machine learning in additional depth
    • Keep learning and active
    With the correct qualifications, you’ll get pleasure from a bright career outlook as a knowledgeable soul. The demand for people with these skills can still increase, and people already in data science roles are bound to see their salaries increase within the future. Data scientists work at intervals in most major industries wherever growth is occurring. Not only did IBM predict the demand for data scientists would grow by 28th in 2020, however, the Bureau of Labor Statistics considers data science within the prime twenty quickest growing occupations and has projected thirty-first growth over the following 10 years.
    Data science groups have folks from various backgrounds like chemical engineering, physics, economics, statistics, mathematics, research, technology, etc. You'll realize several data scientists with a bachelor's degree in statistics and machine learning however it's not a demand to be told Data Science.
    The various edges of Data Science are as follows:
    • The abundance of Positions
    • An extremely Paid Career
    • Data Science is flexible
    • Data Science Makes information higher
    • Data scientists are extremely Prestigious
    • Apache Spark
    • BigML
    • D3 MATLAB
    • Excel
    • ggplot2

    What are the purposes of the Data Science Certification?

    The key objective of Data Science is to extract valuable data to be used in the strategic higher cognitive process, development, analytic thinking, and statement. The key techniques in use are data processing, huge data analysis, data extraction, and data retrieval. The purpose of data science is to create the means for extracting business-focused penetrations from data. This requires an understanding of however worth and data flows in an extremely business, and therefore the ability to use that understanding to find business opportunities.

    What skills are utilized in a Data Science Online Training in Abu Dhabi?

    One of the foremost necessary technical knowledge soul skills is applied math analysis and computing, mining, and process big data sets. This additionally includes extracting the info that's thought valuable. Some information scientists have a pH scale
    • Statistics
    • Programming Language R/ Python
    • Data Extraction, Transformation, and Loading
    • Data wrangle and information Exploration
    • Machine Learning And Advanced Machine Learning (Deep Learning)

    What are the job opportunities after completing the Data Science Certification Course?

    To name many, a number of the foremost common job titles for information scientists include:
    • Business analyst
    • Data Mining Engineer
    • Data designer
    • Data Scientist
    • Senior Data Scientist

    What are the requirements for learning Data Science Certification?

    Data science requires the basics of statistics and mathematics, which should be clear to be able to analyze the problems that are at hand. To solve business problems, you need to have soft skills like team management and control over the projects to meet the deadlines. You will find many data scientists with a bachelor's degree in statistics and machine learning but it is not a requirement to learn data science

    Will Data Science requires coding background?

    You need to possess knowledge of different programming languages, like Python, Perl, C/C++, SQL, and Java, with Python being the foremost common cryptography language needed in data science roles. These programming languages facilitate data scientists to organize unstructured data sets.6
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    Overview of Data Science Certification Training in Abu Dhabi

    Data Science Training in Abu Dhabi is a concepts from mathematics, statistics, calculus, linear algebra, and probability. Following that is a primer on Data Mining and the use of Regression Analysis methods in Data Mining. The concepts and implementation of Python programming to enable Data Mining and Machine Learning are also covered in depth. The use of NLP libraries and OpenCV to code machine learning algorithms is described in great detail. The emphasis on machine learning, deep learning, and neural networks is the course's main highlight. Feedforward and backward propagation in neural networks are thoroughly discussed.

    The implementation of the Activation function, Loss function, and non-linear activation function is elaborated. This course also provides a thorough examination of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), GANs, Reinforcement Learning, and Q learning. This course is a comprehensive package for all IT enthusiasts interested in designing and developing AI applications in their field of study.


    Additional Info

    Introduction:

    Data Science Certificate Program, you can gain global data literacy. This experiential, innovative, and comprehensive programme is tailored to talented individuals seeking a transformative learning experience in data science. Participants will learn about cutting-edge data Science tools and techniques for extracting insights from real-world data. The programme pedagogy is comprised of an interdisciplinary curriculum and interdepartmental collaboration efforts of the University of Toronto's Rotman School of Management, Department of Statistical Sciences, and Department of Computer Science in design and delivery.

    Participants will be able to do the following thanks to this interdisciplinary curriculum:Make use of Internationally Recognized Instructors who have a combination of academic excellence and extensive industry experience.Exposure to examining subjects and concepts from various perspectives, all of which are relevant to the real business world.Look for interesting insights through discussions, assignments, practises, and case studies.Participants who join the Rotman Connect community will be able to gain professional leads and connections through the Rotman School of Management's global online community. The Advanced Data Science Program will provide you with the data-driven skills and confidence you need to gain a competitive advantage in the growing field of data science.


    Who can Learn:

      What exactly is data science?

      Data Science is a branch of computer science that focuses on identifying hidden trends and patterns in structured and unstructured data using a variety of algorithms, tools, scientific methods, and Machine Learning techniques.

      What are the various routes into Data Science?

    • There are several paths that one can take to become a Data Scientist.
    • Data Scientists employ a wide range of Data Science tools and technologies, including programming languages such as R and Python, as well as analysis tools such as SAS.
    • As a prospective Data Scientist, you should be familiar with data analysis, statistical software packages, data visualisation, and working with large datasets.
    • Which programming language is best for data science, and why?

    • Python is the most widely used and preferred programming language in data science.
    • Python is an open-source programming language that is simple to use and learn.
    • Furthermore, it is a dynamic language that supports a variety of paradigms.
    • Aside from this, R and SQL are two other languages used in data science.

    Skills Covered in Data Science Course:

    • Statistics and mathematics Any good Data Scientist will have a solid foundation in both math and statistics.
    • Modeling and analytics.
    • Methods of Machine Learning.
    • Programming.
    • Visualization of data.
    • Curiosity in the mind.
    • Communication.
    • Business savvy.
    • Big data: Large or complex data sets that cannot be managed with traditional data processing software are referred to as big data.
    • That is why data scientists must be familiar with Apache Hadoop or Apache Spark, both of which are open-source distributed processing systems.
    • Data modelling is the process of converting specific data into a database.
    • Data visualisation is the graphic representation of data that is used to show trends and insights.
    • Machine learning is a collection of techniques for predicting and forecasting data.
    • Programming: If you want to automate data manipulation, you must be familiar with programming languages such as Python and R.
    • Statistics: Although you do not need to be a statistician to interpret data, you must be familiar with some form of applied statistics.
    • Data scientists do not work in silos; they are frequently part of larger data science teams that include data engineers, software developers, and others.

    Advatages:

      1. Cut the Fluff, Stick to the Essentials:

    • No one expects a professional data scientist to derive any AI algorithms from first principles.
    • You also don't need to delve deeply into the (relatively) trivial history behind each algorithm, nor do you need to learn SVD (Singular Value Decomposition) or Gaussian Elimination on a real matrix without the assistance of a computer.
    • An academic degree covers so much material that is never used on the job! Yes, you must have an intuitive understanding of the algorithms.
    • When compared to academic researchers or academic counterparts, professional data scientists work in very different domains.
    • Learn what you will require on the job.
    • 2. Learning from instructors with real-world experience rather than PhD scientists:

    • Now, where should you get your training? From PhD academics who have never worked on a real professional project but have published extensively, or from instructors who have worked on real-life professional projects? Teachers and instructors in colleges and universities frequently fall into the former category, and you are extremely fortunate if you have an instructor who has that invaluable component known as industry experience.
    • They will be able to teach you in the context of real-life job experience, which is always exactly what you require.
    • 3. Working with Cutting-Edge Technology Stacks:

    • Now, who is more likely to get you a job: teachers who teach what they studied ten years ago or professionals who work with the most up-to-date tools in the industry? It is undeniably true that people with industry experience can assist you in determining which technologies to learn and master.
    • Academics, on the other hand, may be working with technology stacks that are more than ten years old! Please try to select instructors who have prior work experience.
    • 4. Individual Focus:

    • Individual attention cannot be provided to each student in a college or MOOC with thousands of students.
    • However, it is true that in data science programmes, every student will receive individual attention tailored to their needs, which is exactly what you require.
    • Every student is unique, and they will each have their own interpretation of the projects available.
    • The most significant advantage that such students have over college and MOOC students is the customised attention that is available when batch sizes are less than 30-odd.
    • 5. Guidance for GitHub Project Portfolios:

    • Every college professor will advise you to create a GitHub project portfolio, but they will not be able to give your individual profile their full attention.
    • The reason for this is that they have far too many students and demands on their time to devote to individual project portfolios and actually mentor you in designing and establishing your own project portfolio.
    • However, data science programmes are unique, and instructors can truly mentor you individually in designing your project portfolios.
    • so that you can really stand out and be a cut above the rest of your competition.
    • 6. Mentoring Even After You've Been Placed in a Company and Are Working Alone:

    • Because your domains will be so different, no college professor will be able or even available to help you once you are placed in the industry.
    • However, when industry professionals become instructors, the storey is very different.
    • You can even go to them or contact them for guidance after placement, which is something that most academic professors will not be able to do unless they have industry experience as well, which is extremely rare.
    • 7. Help with Job Placement:

    • People who have worked in the industry understand how important it is to have company referrals in the placement process.
    • It's one thing to make a cold call to a company that doesn't have any internal referrals.
    • It can mean the difference between a successful and unsuccessful recruitment process if you have someone already established within the company you are applying to.
    • Every industry professional will have contacts in a variety of companies, putting them in a unique position to assist you when it comes to job placement opportunities.
    • 8. Learn important but non-technical job skills such as networking, communication, and teamwork:

    • A job in the industry entails far more than just the skills learned in class.
    • You must be able to communicate effectively and work well in groups, which can be guided by industry professionals rather than professors, who will have no experience in this area because they have never worked in the industry.
    • Professionals will know who to guide you on this aspect of your expertise because they have been in that position and have learned the necessary skills in the industry through their job experiences and work capacities.
    • 9. Lower Cost Requirements:

    • It's one thing to be able to pay for your own PhD doctoral tuition.
    • It's quite another to learn the same skills for less than 1% of the cost of a PhD in, say, the United States.
    • Not only is it less demanding financially, but you also don't have to worry about being able to pay off massive student loans through industry work and fat paychecks, which can often come at the expense of compromising your health or family needs.
    • In most cases, the data science programme will provide you with better training than an academic qualification because your instructors will have real-world experience.
    • 10. Significantly Reduced Time Requirements:

    • A PhD degree typically takes 5 years to complete.
    • A data science programme can get you job-ready in a matter of months.
    • Why don't you decide which option is best for you? This is especially true if you already have job experience in another domain or are over the age of 23-25, as completing a full PhD programme could put you on the wrong side of 30 with almost no job experience.
    • Please apply for the data science programme, as time spent working in your 20s is critical for most companies hiring today, as they consider you to be a good ‘çultural fit' for the company environment, especially if you have less than 3-4 years experience.

    Career path od Data Science Course:

    1. A career in data science is both profitable and rewarding.

    2. However, the path to launching or advancing a data science or analytics career is not always straightforward.

    3. Unlike more traditional jobs, becoming a data science professional does not necessitate a technical bachelor's or master's degree.

    4. All you need are the right skills and experience.

    5. You'll learn the ins and outs of data science and analytics career paths and skills in this guide.

    6. Take away advice on how to choose the right data science career for you.

    7. Deciding whether a career in data science is right for you entails more than simply determining whether you enjoy working with data.

    8. It's about determining whether you enjoy working on complex, ambiguous problems and whether you have the aptitude and patience to expand your skill set.


    Roles and Responsibilities:

      Management:

      The Data Scientist plays a minor managerial role in which he assists in the development of a foundation of futuristic and technical abilities within the Data and Analytics field in order to assist various planned and ongoing data analytics projects.

      Analytics:

    • The Data Scientist is a scientific role that plans, implements, and evaluates high-level statistical models and strategies for use in the most complex business issues.
    • The Data Scientist creates econometric and statistical models for a variety of problems such as projections, classification, clustering, pattern analysis, sampling, and simulations.
    • Strategy/Design:

      The Data Scientist plays an important role in the development of innovative strategies to understand the business's consumer trends and management, as well as ways to solve difficult business problems, such as the optimization of product fulfilment and overall profit.

      Collaboration:

      The Data Scientist's role is not a solitary one, and in this position, he collaborates with superior data scientists to communicate obstacles and findings to relevant stakeholders in order to improve business performance and decision-making.

      Knowledge:

    • The Data Scientist also takes the initiative to investigate new technologies and tools with the goal of developing innovative data-driven insights for the business at the quickest possible pace.
    • In this case, the Data Scientist also takes the initiative in assessing and implementing new and improved data science methods for the business, which he submits for approval to senior management.

    Tools Involved in Data Science Training:

    • Notebook Jupyter.
    • RStudio.
    • Zeppelin.
    • Watson Design Studio.

    Certification:

    • SAS Academy for Data Science is a data science training programme run by SAS.
    • Microsoft Certified Solutions Professional (MCSE).
    • Cloudera Certified Professional (CCA).
    • CCP Data Engineer is a Cloudera Certified Professional.
    • Harvard Extension School offers a certificate in data science.
    • AWS Big Data Certification from Amazon.
    • Oracle Certified Business Intelligence (OCBI).

    Payscale:

    1. The median salary for data science in Abu Dhabi is $95,000, which is nearly double the national average.

    2. Even the average salary for data analysts, a more entry-level role, is around $70,000, which is significantly higher than the median salary in the United States.

    3. The average salary for a data scientist is $850,000.

    4. With 5 to 8 years of experience, a mid-level data scientist can earn around $100,000 per year.

    5. Early-career data scientists with 1 to 2 years of experience earn approximately $6,11,000 per year.

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    Key Features

    ACTE Abu Dhabi offers Data Science Certification 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 .
     

    Curriculum

    Syllabus of Data Science Certification Course in Abu Dhabi
    Module 1: Introduction to Data Science Certification with R
    • What is Data Science Certification , significance of Data Science Certification in today’s digitally-driven world, applications of Data Science Certification , lifecycle of Data Science Certification , components of the Data Science Certification 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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    Our Top Hiring Partner for Placements

    ACTE Abu Dhabi 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 Certification training course content, we will arrange the interview calls to students & prepare them to F2F interaction
    • Data Science Certification 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.

    Complete Your Course

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

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

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    About Experienced Data Science Certification Trainer

    • Our Data Science Certification Training in Abu Dhabi. 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 Certification 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 Certification training to the students.
    • We have received various prestigious awards for Data Science Certification Training in Abu Dhabi from recognized IT organizations.

    Data Science Certification Course FAQs

    Looking for better Discount Price?

    Call now: +91-7669 100 251 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
    ACTE
      • Gives
    Certificate
      • 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 .
    • The entire Data Science Certification 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 Certification Course At ACTE?

    • Data Science Certification Course in ACTE is designed & conducted by Data Science Certification experts with 10+ years of experience in the Data Science Certification 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 project experience, job support, and lifetime resources.
    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 Certification batch to 5 or 6 members
    Our courseware is designed to give a hands-on approach to the students in Data Science Certification . 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 76691 00251 / Directly can do by ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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          Job Opportunities in Data Science

          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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