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Data Science Certification Training in Istanbul

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08:00 AM & 10:00 AM Batches

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


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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 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.
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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
  • 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 Istanbul?

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 Istanbul

The Data Science course in IStanbul offers live instructor-led sessions to help you prepare for the role of Data Scientist. Data Scientists are among the best-paid and most sought-after professionals. Our comprehensive Data Science programme includes python programming, machine learning, and other topics. You will work on real-time capstone projects at the end of this online Data Science course.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.

Additional Info


Data science in Istanbul can be defined as a combination of mathematics, business acumen, tools, algorithms, and machine learning techniques that aid in the discovery of hidden insights or patterns in raw data that can be used to make important business decisions.Statistics, tools, and business knowledge are all combined in Data Science. As a result, a Data Scientist must be well-versed in and comprehend these concepts.

In data science, both structured and unstructured data are dealt with. Predictive analytics is also used in the algorithms. Thus, data science is concerned with the present and future. That is, identifying trends based on historical data that can be used to make current decisions, as well as identifying patterns that can be modelled and used to make predictions about how things might look in the future.

Tools Involved in Data Science:

    Extensive knowledge of R:

  • R is used for data analysis, as a programming language, as a statistical analysis environment, and for data visualisation.
  • Python programming: Python is widely used to implement mathematical models and concepts because it has a large number of libraries/packages for building and deploying models.
  • Microsoft Excel:

  • Microsoft Excel is considered a must-have for all data entry jobs.
  • It is extremely useful in data analysis, extracting formulae, equations, and diagrams from a jumbled mess of data.
  • Platform for Hadoop:

  • It is a distributed processing framework that is open source.
  • It is used to manage the processing and storage of large amounts of data.
  • SQL database/programming:

  • It is primarily used for dataset preparation and extraction.
  • It can also be used to solve problems such as graph and network analysis, search behaviour, fraud detection, and so on.
  • Technology:

  • Because there is so much unstructured data available, it is also necessary to understand how to access it.
  • This can be accomplished in a variety of ways, including through APIs and web servers.

Data Science Components in Istanbul:

    Data Science is divided into three parts:

  • Machine Learning entails algorithms and mathematical models, which are primarily used to teach machines to learn and prepare them to adapt to everyday advancements.
  • Time series forecasting, for example, is widely used in trading and financial systems these days.
  • In this case, the machine can predict the outcomes for the next few months or years based on historical data patterns.
  • This is an example of machine learning in action.
  • Big Data & Analytics:

  • Every day, humans generate enormous amounts of data in the form of clicks, orders, videos, images, comments, articles, RSS feeds, and so on.
  • These data are typically unstructured and are referred to as Big Data.
  • Big Data tools and techniques primarily aid in the conversion of unstructured data into structured data.
  • Assume someone wants to keep track of the prices of various products on e-commerce sites.
  • Using Web APIs and RSS Feeds, he/she can access data for the same products from various websites.
  • Then organise them into a structured format.
  • Intelligence in Business:

  • Every business has and generates an excessive amount of data on a daily basis.
  • This data, when carefully analysed and presented in visual reports with graphs, can bring good decision making to life.
  • This can assist management in making the best decision possible after carefully delving into the patterns and details revealed by the reports.

Skills Covered in Data Science:

  • Learn everything there is to know about data structure and data manipulation.
  • Understand and apply linear and nonlinear regression models, as well as classification techniques, for data analysis.
  • Learn about supervised and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline.
  • Use the SciPy package and its subpackages such as Integrate, Optimize, Statistics, IO, and Weave to perform scientific and technical computing.
  • Learn about mathematical computing with the NumPy and Scikit-Learn packages.
  • Learn about the various components of the Hadoop ecosystem.
  • Learn how to work with HBase, its architecture, and data storage, as well as the differences between HBase and RDBMS, and how to partition data using Hive and Impala.
  • Learn about MapReduce and its characteristics, as well as how to ingest data with Sqoop and Flume.
  • Learn about recommendation engines and time series modelling, as well as the principles, algorithms, and applications of machine learning.
  • Learn how to use Tableau to analyse data and create interactive dashboards.

Roles and Responsibilities:

    A data scientist is a person who excels at statistics. Let's take a look at some responsibilities:

  • Data should be gathered from a variety of sources, all of which must be reliable. To make the process easier, the data collection process can be automated.
  • Data cleaning is a critical step in any data analysis project because it consumes the majority of the data scientist's time. Missing data should be filled in, and unnecessary fields should be skipped.
  • Data analysis must be performed correctly in order to identify various trends and patterns in the data.
  • Machine learning should be used to build models in order to thoroughly understand and analyse the data.
  • Training and test datasets must be correctly identified and separated from understanding the impact of data.
  • To understand the data pattern, various models should be combined and thoroughly studied.
  • To make good business decisions, data must be properly organised and understood by everyone on the team.
  • They should be good listeners to the team and observers of various data findings.
  • Data scientists must interpret data carefully because incorrect interpretations can have disastrous consequences for the company.
  • Data scientists should convert collected data, whether structured or unstructured, into a meaningful format so that even an employee from a different department can understand the data.
  • Being a good mathematician aids data scientists in easily segregating data, identifying trends, and identifying correlations.
  • They should be kept up to date on all of the latest data trends in the industry for his benefit.
  • The domain knowledge in which he works is important because it aids in the understanding of the data.
  • This is done to avoid unnecessary data and to consider only necessary data.
  • Data scientists should work with other departments to collect data from their fields and be well-versed in their work.
  • After data analysis, data scientists' insights should be relevant to the domain, and the change should be reflected in the company's profit.
  • Analysis of past data aids in understanding the behaviour of data, and forecasting the future aids in planning the future accordingly.

Career path:

    1. Data Analyst:

  • A data scientist is the most sought-after data science career path among professionals.
  • If you want to become a data scientist, you will be responsible for data collection, analysis, and actionable insights based on large amounts of raw data.
  • If you want to pursue a career in data science, you should be interested in technology and have a strong background in mathematics, statistics, and programming skills.
  • 2. Data Scientist:

  • A data analyst gathers information from various sources and analyses it to gain actionable insights.
  • They are in charge of transforming and manipulating large data sets in order to match the companies' desired analysis.
  • A data analyst recommends various methods and techniques that can assist a company in improving the quality of its data systems.
  • 3. Data Scientist:

  • This is another well-known data science career path that attracts a large number of professionals.
  • A data engineer is in charge of building and maintaining data pipelines that help data scientists access information.
  • They are also in charge of developing new APIs to support the increased data complexity.
  • They collaborate closely with front-end and back-end developers, as well as product managers and analysts.
  • 4. Architect of Data:

  • They are in charge of creating a blueprint for all data management systems.
  • Each company's database must be built and maintained by identifying all potential structural and installation solutions.
  • A data architect is in charge of this task.
  • It is their responsibility to ensure that their company's data solutions are built for performance and to design analytics for multiple platforms.
  • 5. Analyst for Business Intelligence:

  • They convert data into insights that drive business value.
  • This data science career path is best suited for individuals with strong critical thinking and problem-solving skills.
  • Data analytics, data visualisation, and data modelling techniques are used by BI analysts.
  • BI analysts can identify trends that can assist managers in making business decisions that will improve the overall performance of the organisation.
  • 6. Statisticians:

  • A statistician has a keen sense for spotting patterns in data.
  • They are experts in mathematics and statistics who use statistical methods to solve real-world problems.
  • They are in charge of gathering data, identifying trends and connections in data, and communicating the findings to stakeholders.

Advantages of Data Science:

    1. Data Science Training improves candidates' career prospects:

  • Data Science training enables candidates to advance in their careers.
  • We all know that the demand for data science professionals is increasing in almost all major industries.
  • A data scientist is required not only in the leading sectors, but also in the world's most prestigious locations.
  • Most of the world's most prominent business locations offer Data Science jobs.
  • The candidate can obtain a job in Data Science by acquiring data science proficiency, skills, and technology through data science training.
  • 2. With the assistance of Data Science Training, you can obtain certifications in the following demanding Big Data technologies:

  • Data Science training prepares you for the growing demand for Big Data skills and technology.
  • Data Science training equips professionals with data management technologies such as Hadoop, Flume, and machine learning, among others.
  • If a candidate possesses knowledge and proficiency in these critical data skills, they will have an advantage in advancing and competing in their career.
  • When a candidate becomes an expert in Big Data and Data Science technologies, it is simple for them to obtain the top Data Science job Titles with a high salary range.
  • 3. Data Science Training prepares you for the highest-paying Data Science job title with Big Data skills and expertise:

  • There are a variety of job titles created by Big Data and Data Science technologies that pay well in comparison to other IT jobs.
  • These two technologies are not limited to the field of information technology; they are used in all major industries today.
  • As a result, a certified Data Science professional has virtually limitless job opportunities in every field.
  • So, let's take a look at some of the job titles for Data Scientist.
  • 4. With the help of Data science training, a candidate can be hired by one of the following Fortune 500 companies:

  • Many businesses are now looking for Data Science professionals. Facebook, PayPal, eBay, Google, Amazon, Microsoft, and Apple are just a few of the top companies.
  • With the help of Data Science training, a candidate can gain expertise and skills that they can include on their resume as a label to gain entry into these top Fortune companies.
  • If a candidate wishes to be hired by one of these companies, he or she should obtain the necessary Data Science Training.
  • 5. Data Science Training qualifies you to work in new positions:

  • The majority of employers are implementing Big Data and analytics technologies, which will more than double by 2019.
  • As a result, employment of big data and data science professionals will reach an all-time high by 2019.
  • Data science training equips you with the necessary knowledge, skills, technology, and expertise to launch a successful career in a field with a high number of job openings.
  • Big Data and Data Science are vast fields that will not go away in the future.
  • As a result, a career in data science is expected to be long-term.
  • 6. Data Science training is delivered by industry experts rather than PhD scientists:

  • If you decide to pursue Data Science training, is it from a PhD holder who has no experience on a real-world professional project but has published their work, or from an instructor who has expertise in a real-world professional project? It is advantageous to have an instructor who has valuable industry experience in the relevant field.
  • It may be difficult to locate these individuals, but it is advantageous to study under them.
  • They can teach you in the context of job experience in a real-life situation, which is what the majority of candidates require the most.
  • 7. Individual Focus:

  • In a college, however, it is difficult for students to receive personalised attention.
  • However, in Data Science programmes, each student can receive individualised attention based on their needs.
  • Every person is unique and will have their own interpretation of the projects.
  • When the batch size is less than 30, they can get the proper attention from the expert, which is the most significant benefit of Data Science training.


1. The Data Science in Istanbul position is one of the highest-paying jobs of the twenty-first century.

2. The typical salary is $100,000. Starting salaries for those with advanced degrees in data science range from $5000 to $90,000 per year.

3. The salary of a data scientist is determined by experience, education, and industry.

4. The higher the salary, the greater the experience and education.

5. In India, the average salary is 100,000 rupees.

6. It is determined by the location. Data Science jobs aren't going away any time soon.

7. A data science job is one of the most desirable jobs of the twenty-first century.

8. A data scientist must be knowledgeable in a variety of fields in order to excel in his field.

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Key Features

ACTE Istanbul 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 and National Institute of Education (nie) Singapore.


Syllabus of Data Science Certification Course in Istanbul
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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Hands-on Real Time Data Science Certification 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 Istanbul 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.

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About Experienced Data Science Certification Trainer

  • Our Data Science Certification Training in Istanbul. 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 Istanbul from recognized IT organizations.

Data Science Certification Course Reviews

Our ACTE Istanbul 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

Data Science class was really helpful in building my career. They cleared my basic concepts and helped me practice for the interviews. They made sure that I understood all the basics and prepared me for the industry. I am thankful for ACTE the staff for their efforts and determination.


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 Certification Course FAQs

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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 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 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 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 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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