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Data Science Certification Training in Baghdad

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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 Baghdad?

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 Baghdad

Our Data Science Training in Baghdad uses cutting-edge labs and infrastructure to provide you with hands-on experience. In addition, we offer Data Science certification training. After completing the Data Science Training course, we have successfully taught and placed many of our students in large international organisations. Our students receive complete placement assistance from us. For fun Data Science Training in Baghdad, we offer Classroom Training, Weekend Training, and a Fast Song Route. Students can choose the most convenient travel times for themselves.

To summarise data Data Science expertise and data enhancement Begin with the life cycle of the Data Science know-how project. The first stage in the Data Science know-how pipeline workflow is data capture, which involves gathering data, extracting it when necessary, and entering it into the system. The maintenance degree includes data warehousing, data cleansing, data processing, data staging, and data architecture.

Additional Info


Data processing comes next and is one of the Data Science know-how fundamentals. Data scientists, as opposed to data engineers, excel at data exploration and processing. This degree covers data mining, data beauty and clustering, data modelling, and summarising data insights—the strategies that produce powerful data. Following that is data evaluation, which is of equal importance. Here are some examples of exploratory and confirmatory work done by data scientists, as well as regression, predictive evaluation, qualitative evaluation, and textual content mining.This is why there may be no such thing as cookie-cutter data. Data Science expertise—as long as it is used correctly.

SQL Certification is Required in Data Science:

  • SQL (Structured Query Language) is a programming language that is used to perform various operations on data stored in databases, such as updating records, deleting records, developing and enhancing tables, views, and so on.
  • SQL is also the standard for modern large data structures that use SQL as their primary API for their relational databases.
  • Data Science is an all-encompassing approach to statistics
  • We want to extract statistics from the database in order to paint with them.
  • This is where SQL enters the picture. Relational Database Management is an important component of Data Science.
  • A Data Scientist can use SQL commands to control, define, manipulate, create, and query a database.
  • Many cutting-edge industries have prepared their product statistics administration with NoSQL generation, but SQL remains the best option for many enterprise intelligence tools and in-office operations.
  • SQL is used to model many of the database structures.
  • This is why it has become popular for a wide range of database structures.
  • Modern massive data structures, such as Hadoop and Spark, also make use of SQL best for maintaining relational database structures and processing dependent statistics.

The Benefits of Data Science Certification Training:

    1. Increases the predictability of commercial enterprises:

  • Increases enterprise corporation predictability When a company invests in structuring its data, it can use what is known as predictive analysis.
  • With the assistance of data scientists, it is possible to use generation in conjunction with Machine Learning and Artificial Intelligence to Work with the data that the company has and, as a result, perform more specific analyses of what is to come.
  • As a result, you increase the predictability of the commercial corporation and can make decisions today that will affect the destiny of your enterprise corporation.
  • 2. Guarantees real-time intelligence:

  • Ensures real-time intelligence data of scientists can Work with RPA experts to pick out the assets of the one-of-a-kind data in their enterprise corporation and create automatic dashboards, which may be searching for the maximum of those data in real-time in a secure manner.
  • This intelligence is critical for your company's managers to make more accurate and timely decisions.
  • 3. Prefers the advertising and income location:

  • Statistics-driven favours the marketing and marketing and earnings area These days, marketing is a well-known term.
  • The reason is straightforward: only with data can we provide solutions, communications, and products that are likely to meet customer expectations.
  • As we've seen, data scientists can combine data from a variety of sources, providing their team with even more accurate insights. With Data Science, this is a possibility.
  • 4. Increases data security:

  • Improves record security Improves data security One of the benefits of Data Science is that the work is completed within the realm of data security.
  • In that sense, there may be an infinite number of possibilities.
  • Data scientists, for example, work on fraud prevention structures to keep your company's clients safe. On the other hand, he can examine typical patterns of behaviour in a corporation's structures to identify potential architectural flaws.
  • 5. Aids in the interpretation of complex facts:

  • Aids in the interpretation of complex records assists in the interpretation of complex data statistics Science is an excellent solution, even though we need to move one-of-a-kind data to better understand the commercial corporation and the market.
  • Depending on the device we use to collect data, we can combine data from "physical" and "digital" assets for better visualisation.
  • 6. Makes the decision-making process easier:

  • Facilitates the decision-making system Facilitates the route selection-making system Based on what we've learned thus far, you might already believe that one of the benefits of Data Science is improving the selection-making system.
  • This is due to the fact that we can create a device that can view data in real time, allowing business executives to be more agile.
  • This is accomplished both with the beneficial resource of using dashboards and with the beneficial resource of using projections that may be feasible with the data scientist's data solution.

Data Science Certification Training in the Future :

    Data Science is a constantly evolving endeavour that is expected to grow in demand in the near future.

    Some of the critical element changes are listed below:


  • With the novel growth of the data era, the overall normal overall performance of the predictive algorithms will improve over time as more data are available to attract inference upon.
  • This phenomenon is fueled by the beneficial resource of the rise of Social Media and IoT-primarily based devices, which generate massive amounts of data.
  • Algorithms:

  • Machine Learning algorithms such as Genetic Algorithms and Reinforcement Learning algorithms are expected to improve over time, resulting in more intelligent structures.
  • Computing Distributed:

  • With blockchain generation advancements, TPU (Tensor Processing Unit) advancement, and faster GPU (Graphics Processing Unit) to be had within the cloud, Data Science sees a future wherein more effective computational hardware aids algorithms of increasing complexity.
  • Data Science Certification Training for Career Advancement in Baghdad:

  • The beneficial useful resource of using data may dominate the twenty-first century.
  • Data Science has evolved into an essential component of many organisations and industries.
  • It provides valuable insights into customer behaviour, which can lead to increased conversions, greater real-world market evaluation for competitive advantage in pricing strategies or product development, improved operational efficiency, and reduced risk publicity via accurate forecasting models.

Skills required to advance to the level of Data Scientist:

  • As stated in the preceding section, data science is a difficult task.
  • As a result, it necessitates mastery of multiple sub-fields, which collectively upload as a bargain because the complete statistics are required to be a Data Scientist.
  • 1. Applied mathematics:

  • The first and most important field of study to become a Data Scientist is mathematics; more specifically, probability and statistics, linear algebra, and a few basic calculus.
  • 2. Frameworks for Machine Learning:

  • Machine Learning is an important part of Data Science, and its implementation includes excellent libraries and frameworks, the statistics of which can be invaluable to any Data Scientist.
  • A number of the most commonly used Machine Learning frameworks are listed here.
    • Numpy:

    • This is a library that makes linear algebra and data manipulation simple to implement.
    • Pandas:

    • This library is used to load, manage, and store data. This is also used in data manipulation.
    • Matplotlib:

    • This is one of the most commonly used data visualisation libraries.
    • Seaborn:

    • This is a wrapper for Matplotlib, and it is used to visualise more complex data.
    • Sklearn:

    • This is used to learn how to use and implement the most of the device's algorithms and data preprocessing strategies.
    • Tensorflow:

    • This is a comprehensive learning framework supported by the helpful resource of Google that allows for the simple implementation of numerous types of neural networks.
    • PyTorch:

    • Similar to TensorFlow, this is a thorough understanding of a commonly used framework.
    • Keras:

    • This is a wrapper that works in tandem with TensorFlow to make Deep Learning strategies especially simple to implement.
    • OpenCV:

    • This is a computer vision framework that is commonly used for image processing and manipulation.
    • Statistics:

    • It is critical in EDA and developing algorithms to perform statistical inference on data.
    • Furthermore, the majority of Machine Learning Algorithms rely on data as their primary building blocks.
    • Linear Algebra:

    • Working with large amounts of data requires the use of high-dimensional matrices and matrix operations.
    • The data that the model accepts and the simplest that it provides as output are in the shape of matrices, and any operation performed on them makes use of the fundamentals of Linear Algebra.
    • Calculus:

    • Calculus is extremely important in Data Science because it includes Deep Learning.
    • Gradient calculation is critical in Deep Learning and is performed at each step of computation in Neural Networks.
    • This necessitates the use of reliable statistics on differential and integral calculus.

    3. Programming languages (R and Python):

  • Even though any programming language can be used for any type of logical use case, which includes Data Science, the most commonly used languages are R and Python.
  • Both of those languages are open supply and thus have widespread network support, have multiple libraries advanced with Data Science in mind, and are especially simple to look at and use.
  • A Data Scientist cannot work out any shape of algorithmic or mathematical statistics of the data without the statistics of programming languages.
  • 4. An Appropriate Programming Environment:

  • Because good programming statistics is one of the most important requirements for Data Science, there must be a platform to write and execute the code.
  • The IDE, or Integrated Development Environment, is the name given to this platform.
  • There are numerous IDEs to choose from, and some of them are particularly advanced for Data Science.
  • This article discusses the Top 10 Python IDEs.
  • 5. SQL:

  • Databases are critical components of the Data Science project because they are the most appropriate method for storing data.
  • Comprehensive statistics for 1 or more database generation, such as MySQL, MariaDB, PostgreSQL, MS SQL Server, MongoDB, Oracle NoSQL, and so on.
  • 6. Algorithmic Understanding:

  • Even though Data Science does not generally include the development and layout of Algorithms, as some distinct programmes of Computer Science do, it is nonetheless critical for a Data Scientist to have legitimate statistics of Algorithms.
  • This is because, at the end of the day, Data Scientists are programmers who are expected to develop packages that can derive massive insights from data.
  • With algorithmic statistics, the Data Scientist could write massive green code that saves time and delivers and, as a result, is an alternative value.

Trends in Data Science:

  • Since its inception within the period, the endeavour of Data Science has been growing.
  • With the passage of time, the growing modern generation is being included in the endeavour.
  • Some of the more notable ultra-modern-day additions are listed below:

    Artificial Intelligence (AI):

  • Machine Learning is one of the many aspects of Data Science.
  • Deep Learning, on the other hand, has been the most recent and one of the most significant additions to the Data Science project, thanks to its increased parallel compute capabilities.
  • Computing at the Periphery:

  • Edge computing is a cutting-edge concept that is linked to IoT. (Internet of Things).
  • The Data Science pipeline of data series, shipping, and processing is placed within the data shipping route by edge computing.
  • This is possible with IoT and is now considered part of Data Science.
  • Security:

  • Within the virtual space, security has been a primary responsibility.
  • Malware injection and the concept of hacking are now fairly commonplace, and all virtual structures are vulnerable to it.
  • Fortunately, there are a few ultra-modern-day Data Science enhancements that exercise Data Science strategies to save you from the exploitation of virtual structures.
  • For example, in comparison to standard algorithms, Machine Learning strategies have established greater functionality in detecting laptop viruses or malware.

Data Science Online Training Roles:

  • The term "Data Science" refers to a vast collection of structured, semi-structured, or unstructured heterogeneous data. Databases are generally incapable of dealing with such massive datasets. As previously stated, data is a critical component of data science.
  • As a general rule, “the larger the data, the greater the insights.” As a result, Data Science is a critical component of the Data Science project.
  • Data Science is distinguished by the use of the beneficial useful resource of using its range and quantity, both of which are critical for Data Science. Data Science captures the complex styles of Data Science with the useful resource of growing Machine Learning Models and Algorithms.
  • Data Science is a type of project that can be completed in almost any company to solve complex problems. Every company applies Data Science to one-of-a-kind software in order to solve a one-of-a-kind problem.
  • Some organisations rely on Data Science and Machine Learning strategies to solve a wide range of problems that would otherwise be intractable.
  • Some of these Data Science packages, as well as the agencies that support them, are listed below.
  • Search Engine Results (Google):

  • When a person searches for something on Google, complex Machine Learning algorithms determine which outcomes are most likely to be applicable for the duration of the search (s).
  • These algorithms help to rank pages so that the most relevant data is provided to the user at the push of a button.
  • Spotify's Recommendation Engine:

  • Spotify is a track streaming service that is well-known for its ability to recommend tracks based on the user's preferences. This is an excellent example of Data Science in action.
  • Spotify's algorithms examine the person's taste in track and recommend similar track in the future based on the data generated with the useful resource of using all and sundry over the years.
  • This could help the company gain more customers because it is easier for people to use Spotify because it does not require a lot of attention.
  • Google Assistant and other intelligent digital assistants:

  • Google Assistant, like other voice or text-primarily based virtual assistants (also known as chatbots), is an example of superior Machine Learning algorithms in action.
  • These algorithms can convert a person's speech (regardless of unique accents and languages) to textual content, understand the context of the textual content/command, and provide relevant data or carry out a desired task, all while speaking to the device.
  • Gmail Spam Filter:

  • The junk mail filters in our emails are another important piece of Data Science software that we use on a daily basis.
  • These filters routinely separate junk mail emails from the rest, resulting in a far cleaner email experience for the user. Data Science, like the other programmes, is an important building block in this case.
  • Filter for Abusive Content and Hate Speech (Facebook):

  • Similar to an unsolicited mail filter, Facebook and other social media platforms use Data Science and Machine Learning algorithms to remove abusive and age-restricted content from the unintended audience.
  • Automatic Detection of Piracy (YouTube):

  • Most videos that are most likely uploaded to YouTube are actual content fabric material created with the beneficial useful resource of employing content fabric material creators.
  • However, because this is YouTube's policy, pirated and copied movies are frequently uploaded. Due to the sheer volume of ordinary uploads, it is not possible to manually discover and remove such pirated movies. This is where Data Science is used to detect and remove pirated movies from the platform on a regular basis.

What exactly is data science?

Data Science is a multidisciplinary endeavour that employs scientific inference and mathematical algorithms to extract large amounts of statistics and insights from large amounts of structured and unstructured data. These algorithms are carried out by computer programmes, which are typically run on powerful hardware due to the large amount of processing required. Data Science is a conglomeration of statistical mathematics, device learning, data evaluation and visualisation, area statistics, and laptop Data Science expertise.

As implied by the name, the most important component of Data Science is “Data” itself. No amount of algorithmic computation can yield massive insights from erroneous data. Data Science expertise encompasses a wide range of data types, such as image data, textual content data, video data, time-based data, and so on. Our Data Science Training in Baghdad is well-equipped with labs and excellent infrastructure to provide you with hands-on experience. We also offer Data Science certification training.

Online Data Science Training Tools :

    We'll learn about the major features, benefits, and a comparison of various data science tools:

  • SAS
  • Spark (Apache)
  • BigML
  • D3.js
  • Excel
  • ggplot2


1. The Data Science project is one of the highest-paying jobs in the software programme software industry.

2. It is also the best-paying job with the least amount of relevant Work experience when compared to three distinct challenges within the software programme software area, as determined by the parent.

3. With 50,000 positions available, Baghdad is the second-highest country for recruiting employees in the field of data science or data analytics, etc.

4. Trailing only the United States. Demand for data experts is equally competitive in large corporations, the e-commerce industry, and even start-ups.

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Key Features

ACTE Baghdad 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 Training in Baghdad
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 Baghdad 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

Get Certified

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

Get Certified

About Experienced Data Science Certification Trainer

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

Data Science Certification Course Reviews

Our ACTE Baghdad 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

Looking for better Discount Price?

Call now: +91 93833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
    • Gives
    • For Completing A Course
  • Certification is Accredited by all major Global Companies
  • ACTE is the unique Authorized Oracle Partner, Authorized Microsoft Partner, Authorized Pearson Vue Exam Center, Authorized PSI Exam Center, Authorized Partner Of AWS and National Institute of Education (NIE) Singapore
  • The entire Data Science 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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