Data Science Course in Ahmedabad - 100% Job Oriented Training
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Data Science Training in Ahmedabad

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Weekdays Regular

08:00 AM & 10:00 AM Batches

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


Weekend Regular

(10:00 AM - 01:30 PM)

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  • Start learning key data analysis tools such as Microsoft Excel, Python, SQL and R to know about data analysis.
  • We train students to get full knowledge in the course and get placed in top rated companies.
  • We provide you the skills and cover all the topics like Statistics, Coding, Business Intelligence, Data Structures, Mathematics, Machine Learning, Algorithms, etc.
  • Data analysis with different tools and techniques for structured and unstructured data.
  • Develop a descriptive and predictive analysis with better understanding and Build models for day-to-day applicability.
  • Work with different sources of data generation.
  • Concepts: Data Science, significance of Data Science in today’s digitally-driven world, components of the Data Science lifecycle, big data and Hadoop, Machine Learning and Deep Learning, R programming and R Studio, Data Exploration, Data Manipulation, Data Visualization, Logistic Regression, Decision Trees & Random Forest, Unsupervised learning, Association Rule Mining & Recommendation Engine, Time Series Analysis, Support Vector Machine - (SVM), Naïve Bayes, Text Mining, Case Study.
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Course Objectives

The potential for quantum computing and data science is big in the expectation. Machine Learning may additionally process the data abundant quicker with its accelerated learning and advanced capabilities. Supported by this, the time required for establishing advanced issues is considerably decreased. The purpose of the Data Scientist is presently a buzzworthy profession. It reaches within the marketplace and gives opportunities for people that study data science to make valuable contributions to their firms and organizations at giant.

Data scientists carefully use their skills in maths, statistics, programming, and alternative connected subjects to organize giant information sets... Data science is high in the order and explains however digital data is remodeling businesses and serving them to make chiseler and important selections. Therefore digital data is throughout for people that try to work as data scientists.

The demand for people with these skills can still increase, and communities already in data science roles are supported to see their salaries rise within the long run. Data scientists work on intervals in most major industries wherever growth is happening. Not only did IBM predict the demand for data scientists would start by 28th in 2020, however, but the Bureau of Labor Statistics also estimates data science within the excellent 20 quickest developing controls and has projected thirty-first increase over the following 10 years.

Data science challenges the fundamentals of statistics and arithmetic, which should be clear to be able to analyze the issues that are at hand. to resolve business problems, you would like to possess soft skills like team management and control over the projects to fulfill the deadlines. you may find many data scientists with a bachelor's degree in statistics and machine learning but it's not a requirement to learn data science.

  • The abundance of Positions.
  • An extremely Paid Career.
  • Data Science is flexible.
  • Data Science Makes information higher.
  • Data scientists are extremely Prestigious.

You want to possess an understanding of various programming languages, like Python, Perl, C/C++, SQL, and Java, with Python being the foremost general cryptography language required in data science roles. These programming languages help data scientists to arrange unstructured data sets.

  • Data Mining Engineer.
  • Data designer.
  • Data Scientist.
  • Senior Data Scientist.

What skills are utilized in a very Data Science training course?

  • Programming Language R/ Python.
  • Data Extraction, Transformation, and Loading.
  • Data wrangle and knowledge Exploration.
  • Machine Learning And Advanced Machine Learning (Deep Learning).

How will a Fresher begin a career in an extremely Data Science training course?

  • 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.

What are the tools utilized within the Data Science training course?

  • Apache Spark.
  • BigML.
  • D3 MATLAB.
  • Excel.
  • Ggplot2.

What is the essential salary for Data Science software?

The average data scientist's salary is ₹698,412. A Beginner-level data specialist can get throughout ₹500,000 p.a. with but one year of experience. Beginner-level data specialists with 1 to 4 years of experience become about ₹610,811 per annum.

What purposes of Data Science training course?

The objective of Data science is to construct the means for extracting business-focused insights from data. This needs an understanding of how value and knowledge flow in an extremely business, and therefore the ability to use that knowledge to spot business opportunities.

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Overview of Data Science Training in Ahmedabad

Among the most popular and in-demand career paths for skilled professionals, data science continues to grow and develop. The best data professionals in the world today understand that data analysis, data mining, and programming skills are no longer enough. Data scientists must study the entire spectrum of the data science life cycle, as well as have flexibility and an understanding of how to maximize returns at every phase of the process in order to uncover useful intelligence for their organizations. The study of data is known as data science. Analyzing, visualizing, managing, storing, and managing data to generate insights is what it involves. Data-driven decisions can be made using these insights. Using both structured and unstructured data is necessary for Data Science applications. Statistics, mathematics, and computer science are at the foundation of this multidisciplinary field. Having an abundance of data science jobs and a lucrative pay-scale make it one of the most sought-after jobs.


Additional Info

Why is Data Science Important?

Magic happens when data is used. Data can assist industries in making careful decisions. Raw data is turned into meaningful insight by data science. Thus, industries require data science.. Data Scientists are wizards who are able to create magic through data.

A skilled Data Scientist will be able to extract valuable information from any data that he is presented The company benefits from his assistance. Data-driven decisions are a crucial part of the company, and he is the expert in this area.

An expert in numerous underlying areas of statistics and computer science, a Data Scientist is an expert. The ability to analyze problems helps him solve business problems.

Roles & Responsibilities of a Data Scientist

Business stakeholders work closely with data scientists to understand their goals and determine how data can be used to meet those goals. They design data modeling processes, develop algorithms and predictive models to extract the data the business needs and help analyze and share insights with peers. While each project is different, the process for gathering and analyzing data generally follows the below path:


The Data Scientist plays a minor managerial role. He assists in constructing the proficiency base of data analytics projects and provides support for numerous ongoing and planned projects.


The Data Scientist performs a scientific role, planning, implementing, and assessing statistical models and strategies for both internal and external business applications. An economist or statistician develops models for various problems with econometrics and statistics such as projection, classification, clustering, pattern analysis, sampling, simulations, etc.


In addition to helping businesses understand consumer trends and management, the Data Scientist also helps solve business problems, such as the optimization of product fulfillment and entire profits.


In this position, a data scientist works towards enhancing business performance and decision-making by collaborating with superior data scientists.


Data Scientists also take the lead in exploring new technologies and tools with the goal of providing the business with the most insightful information possible through data-driven insights. In this situation, the Data Scientist also uses initiative in assessing and utilizing new and enhanced data science methods for the business, which he delivers to senior management of approval.

Other Duties:

In addition, Data Scientists perform tasks that are assigned to them by a Senior Data Scientist, Head of Data Science, or Chief Data Officer.

Essential Data Science Skills

Most data scientists use the following core skills in their daily work:

Statistical analysis:

Identify patterns in data. This includes having a keen sense of pattern detection and anomaly detection.

Machine learning:

Implement algorithms and statistical models to enable a computer to automatically learn from data.

Computer science:

Apply the principles of artificial intelligence, database systems, human/computer interaction, numerical analysis, and software engineering.

Core skills used by most data scientists include:

Statistical analysis:

Patterns can be identified in data. Pattern detection and anomaly detection are among the most important skills.

Machine learning:

Automate the process of learning from data by using algorithms and statistical models.

Computer science:

Use the principles of computer science, artificial intelligence, database design, human-computer interaction, and numerical analysis.


Analyzing large datasets and writing computer programs to uncover answers to complex problems. Programming skills including Java, R, Python, and SQL are crucial for data scientists.

Data storytelling:

Use data to communicate actionable insights to a non-technical audience.

An organization's ability to make sound decisions relies on the contributions of data scientists. Thus, their "soft skills" must include the following.

Business intuition:

Get to know your stakeholders so you can fully understand the challenges they would like to overcome.

Analytical thinking:

Analyze abstract business issues to find analytical solutions.

Critical thinking:

Make a decision based on an objective analysis of the facts.


Discover patterns and solutions within the data by looking beyond what's on the surface.

Interpersonal skills:

A company's communication must reach a diverse audience at every level.

Benefits of data science

Both data and science that decodes it are valuable. Currently, data is worth more than oil, and billions of bytes are being generated every day. Organizations in many different industries will depend heavily on data scientists in the future.

Data without science is nothing.

Reading and analyzing data is necessary. To be able to make data-driven discoveries, it is vital to possess a quality of data and to know how to interpret it.

Data will help to create better customer experiences.

Machine learning will be the enabling technology for the development of products for goods and services that customers will love. For example, an eCommerce company could discover their customer personas by analyzing their purchase history with a recommendation system.

Data will be used across verticals.

Consumer goods, tech, and healthcare are not the only areas where data science is used. From banking and transportation to manufacturing, there will be a high demand for optimizing business processes using data science. A whole new world of opportunities will open up for anyone who wants to become a data scientist. The future belongs to data.

Why do you want to learn data science

5 valuable reasons to pursue data science as a career

Here are the 5 reasons why you must learn data science.

Great career trajectory with data science – Yes, you will have rewarding career growth in this field. In today's scenario, and for the foreseeable future, data scientists bring a lot of value to organizations.

Great potential to branch out with different options – You can become a data scientist, an analyst, or a ML engineer, or you can even become a data scientist manager.

Highest salary takeaway quotient – As a Data scientist, you can expect to take away a great salary package. Due to the critical role and responsibilities of data scientists, they often receive excellent salaries, sometimes even substantially above market rates.

Become a decision-maker – Not every job opportunity will give you the power to make informed business decisions. That is the core responsibility of a data scientist. It's a great way to get your creative juices flowing. There is no talent pool in the ecosystem, so credibility will always be rewarded.

Less competitive because it is a highly analytical role – Competition is less, but demand is not. The talent pool for these positions is very limited, so hiring is always a challenge for businesses. Your unique skill set will make you less competitive with others in your organization when you join in.

Data Scientist Qualifications Companies Look For in a Candidate

Data Scientist roles and responsibilities include identifying business trends and changes through advanced Big Data Analytics and using a variety of techniques to interpret results from multiple data sources through statistical analysis, data aggregation, and data mining. This is a very important role, and most companies expect some or all of the following Data Scientist skills in candidates before hiring them.

Typical responsibilities of a Data Scientist include identifying business trends and changes through advanced Big Data Analytics, data aggregation, and data mining, and interpreting results from multiple data sources. Companies look for these skills in candidates before hiring them, as this is a very important role.

  • Problem-solving aptitude with a natural inclination
  • Possess experience with/knowledge of statistical programming languages, including R, Python, SLQ, etc., for analyzing and extracting information from data
  • Knowledge of and experience with data architectures
  • The ability to analyze and analyze the pros and cons of various Machine Learning techniques including decision trees, clustering, artificial neural networks, etc.
  • A working knowledge of and experience with advanced statistical techniques, such as regression, distribution properties, graphical analysis, etc.
  • Collaboration between teams can be enhanced by good communication skills
  • New technologies compel you to learn them and master them
  • Knowledge of several programming languages, including Java, JavaScript, C, C++, etc.
  • Data mining experience including GLM/regression, social networks analysis, text mining, etc.
  • Working knowledge of major web services, such as S3, Spark, and Redshift.
  • An understanding of distributed data and computing tools, including MapReduce, MySQL, Hadoop, Spark, and Hive.
  • Use of data visualization tools such as D3, ggplot, Periscope or others to showcase data to stakeholders
  • Job Roles in Data Science

  • Data Analyst
  • Data Engineers
  • Database Administrator
  • Machine Learning Engineer
  • Data Scientist
  • Data Architect
  • Statistician
  • Business Analyst
  • Data and Analytics Manager
  • 1. Data Analyst

    An analyst's responsibilities include analyzing huge amounts of data, visualizing and munging it, and processing it. Occasionally, they must also query the databases. Data analysts need to be able to optimize their work. In order to leach information from some of the biggest databases without destroying them, they must devise and modify algorithms.

    How to become a Data Analyst?

    The most popular technologies for data analysis are SQL, R, SAS, and Python. This makes certification in these areas a great asset when looking for a job. Problem-solving skills are also important.

    2. Data Engineers

    To enable data scientists to run their algorithms on stable and highly optimized data systems, data engineers build and test scalable Big Data ecosystems for businesses. Additionally, data engineers improve the efficiency of databases by replacing outdated systems with newer, more advanced versions of current technology.

    How to become a Data Engineer?

    Technology requisites that you should become proficient in include Hive, NoSQL, Ruby, Java, C++, and Matlab if you are interested in a career as a data engineer. Also, you need to be familiar with popular APIs for data and ETL tools.

    3. Database Administrator

    Database administrators are responsible for the proper functioning of all databases in an organization and grant or revoke their services according to the requirements of employees. Backups and recoveries of databases are also their responsibility.

    How to Become a Database Administrator?

    Backup and recovery of databases, data security, data modeling and design are some of the competencies essential to a database administrator. A good disaster management skill is a plus.

    4. Machine Learning Engineer

    Currently, there is a great demand for engineers with machine learning skills. Nevertheless, there are challenges associated with the job profile. Besides having a thorough understanding of SQL, REST APIs, and other powerful technologies, machine learning engineers need to perform A/B testing, construct data pipelines, and implement computational algorithms such as classification and clustering.

    How to Become a Machine Learning Engineer?

    As a start, you ought to be familiar with some technologies such as Java, Python, and JS. You should also be familiar with mathematics and statistics. It is much easier to crack a job interview once you have mastered both.

    5. Data Scientist

    By analyzing and processing data, data scientists are able to come up with the most effective solutions to business challenges. To provide actionable insights, they undertake predictive analysis and run a fine-tooth comb through "unstructured/disorganized" data. These companies can also do this by identifying trends and patterns that can help them make better decisions.

    How to Become a Data Scientist?

    As a data scientist, you must be proficient in R, MatLab, SQL, Python, and other related technologies. It can also help if you have a higher degree in mathematics or computer engineering, etc.

    6. Data Architect

    Creating data architectures for integrating, centralizing, and safeguarding data is what a data architect does. Additionally, they ensure that the data engineers have access to the best tools and systems.

    How to Become a Data Architect?

    Data architecture requires knowledge of data warehouses, data modeling, and extraction, transformation, and loan processes (ETL). Besides Hive, Pig, and Spark, you should also have a good understanding of them.

    7. Statistician

    Statistics suggests that a statistician has a good understanding of statistical theories and data organization. Not only do they extract and offer valuable insights from the data clusters, but they also help create new methodologies for the engineers to apply.

    How to Become a Statistician?

    Statisticians must be passionate about logic. As well as SQL, data mining, and other machine learning technologies, they are also proficient with a variety of database systems.

    8. Business Analyst

    In comparison with other data science jobs, the role of a business analyst is a bit different. Although they have a good understanding of how data-oriented technologies work and how to handle large amounts of data, they also separate the high-value data from the low-value data. By defining the business insights that can be gained by connecting Big Data to business growth, they outline how data and analytics can be used.

    How to Become a Business Analyst?

    The role of the business analyst is to serve as a conduit between data engineers and management executives. Therefore, they should be knowledgeable about business finances, business intelligence, and IT technologies, such as data modelling, visualization tools, and so on.

    9. Data and Analytics Manager

    Managers assign duties according to team skills and expertise to their teams in data science operations. In addition to management, their strengths should include SAS, R, SQL, etc.

    How to Become a Data and Analytics Manager?

    A strong work ethic, excellent social skills, and excellent leadership qualities are essential. You should also be good at data science technologies like Python, SAS, R, Java, etc.

    Data Science Certifications

    Using data to drive data driven decisions has never been more important than now. Data scientists need to be able to analyze data and communicate results. IBM's professional certificate helps individuals interested in machine learning and data science develop skills and experience relevant to their careers.

    The successful completion of these courses will give you the confidence to launch into an exciting career in data science, thanks to your portfolio of data science projects.

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    Key Features

    ACTE Ahmedabad offers Data Science Training in more than 27+ branches with expert trainers. Here are the key features,
    • 40 Hours Course Duration
    • 100% Job Oriented Training
    • Industry Expert Faculties
    • Free Demo Class Available
    • Completed 500+ Batches
    • Certification Guidance

    Authorized Partners

    ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS and National Institute of Education (nie) Singapore.


    Syllabus of Data Science Course in Ahmedabad
    Module 1: Introduction to Data Science with R
    • What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.
    • Hands-on Exercise - Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.
    Module 2: Data Exploration
    • Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.
    • Hands-on Exercise -Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.
    Module 3: Data Manipulation
    • Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf.
    • Hands-on Exercise -Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.
    Module 4: Data Visualization
    • Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.
    • Hands-on Exercise -Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.
    Module 5: Introduction to Statistics
    • Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.
    • Hands-on Exercise -– Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.
    Module 6: Machine Learning
    • Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.
    • Hands-on Exercise -Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.
    Module 7: Logistic Regression
    • Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression
    • Hands-on Exercise -Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.
    Module 8: Decision Trees & Random Forest
    • What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.
    • Hands-on Exercise -Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.
    Module 9: Unsupervised learning
    • What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.
    • Hands-on Exercise -Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.
    Module 10: Association Rule Mining & Recommendation Engine
    • Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.
    • Hands-on Exercise -Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.
    Module 11: Introduction to Artificial Intelligence (self paced)
    • introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.
    Module 12: Time Series Analysis (self paced)
    • What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.
    • Hands-on Exercise -Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.
    Module 13: Support Vector Machine - (SVM) (self paced)
    • Introduction to Support Vector Machine (SVM), Data classification using SVM, SVM Algorithms using Separable and Inseparable cases, Linear SVM for identifying margin hyperplane.
    Module 14: Naïve Bayes (self paced)
    • what is Bayes theorem, What is Naïve Bayes Classifier, Classification Workflow, How Naive Bayes classifier works, Classifier building in Scikit-learn, building a probabilistic classification model using Naïve Bayes, Zero Probability Problem.
    Module 15: Text Mining (self paced)
    • Introduction to concepts of Text Mining, Text Mining use cases, understanding and manipulating text with ‘tm’ & ‘stringR’, Text Mining Algorithms, Quantification of Text, Term Frequency-Inverse Document Frequency (TF-IDF), After TF-IDF.
    Module 16: Case Study
    • This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.
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    Hands-on Real Time Robotic Process Automation (RPA) Projects

    Project 1
    Optimizing Product Price Project

    Price optimization uses data analysis techniques to pursue two main objectives Understanding how customers will react to different pricing strategies for products and services.

    Project 2
    Boston Housing Predictions Project

    The goal of this project is to create a regression model that is able to accurately estimate the price of the house given the features and made for predicting.

    Project 3
    Color Detection with Python Project

    In this color detection Python project, we are going to build an application through which you can automatically get the name of the color by clicking on them.

    Project 4
    Road Lane Line Detection Project

    The goal of this project is to build up a simple image pipeline, which allows detecting lane lines in simple conditions: sunny weather, good visibility, no cars in sight, only straight lanes.

    Our Best Hiring Placement Partners

    ACTE Ahmedabad for affirmation and guaranteed situations. Our work situated classes are educated by experienced confirmed experts with broad certifiable experience. All our best around down to earth than hypothesis model.
    • ACTE has restricted with numerous enlistment offices and organizations likewise is an authority accomplice of many top organizations which is a famous occupation gateway in Ahmedabad.
    • Also toward the finish of the course will lead a test which is set up by our IT proficient. whoever scores over 90% on the test he will end up being an affirmed progression.
    • Get the most recent CV from the applicants and direct telephonic meetings whenever required and afterward meet with qualified applicants.
    • We give composed references to the applicants on demand on bid for employment.
    • The more youthful age for the most part stays away from stage talks and a mainstream term related with this inclination is called 'stage dread'. With appropriate preparing given in the ACTE, dread is wiped out and applicants start to comprehend that it's anything but a joy to pass on their sentiments to the crowd and this training assists the understudies to confront the questioner with most extreme certainty.
    • To go to a meeting and become fruitful, the Learners should have certain abilities like inclination, show, etc. Our arrangement preparing programs directed by the situation cell centers to foster fundamental work and meeting abilities for the understudies subsequently improving their shots at getting chosen in the meeting.

    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 Proficient Data Science Mentor

    • Our Data Science Training in Ahmedabad. Mentors have more years of authority around here, our mentors are outstandingly gifted and will ensure that the students totally like the subjects being taught.
    • ACTE assist our coaches in leftover energy with the most forward-thinking types of progress in teaching, investigation, and development to outfit our students with a world class learning environment.
    • By offering critical information into requests questions and driving gatherings through reproduced interviews, our Mentors guide up-and-comers in building a specialist CV and boosting their conviction.
    • To ensure our up-and-comer's finished ecstasy, our mentors have made a start to finish course that meets their work needs and standards.
    • Our instructors give a blend of class, teacher drove on the web, and E-learning decisions to ensure that competitors get adjusted tutoring and advance their assumption to learn and adjust.
    • Lecturers will provides both Theoretical and practical knowledge with real-time project works.

    Data Science Course Reviews

    Our ACTE Ahmedabad 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

    Best institute for Data Science Training in Ahmedabad .The ACTE trainers are all good and covers all the syllabus on time.And placement cell also help us to conducting the interviews


    Best DATA SCIENCE training institute in Tambaram with Realtime client projects and dedicated support team. I have taken Data science training on this January and completely happy with their teachings, projects and job support after the course completion. It's a One stop destination for your data science And AI training in BTM Layout.



    ACTE for your career switch to Data Science..They have well experienced Trainers in ACTE who can make you industry ready. Curriculum is quite unique and includes current industry needs. They give very good job assistance also.


    Software Engineer

    Its a good institute for Data Science in Porur. The teaching staff is good, they give us day wise assignments which helped me to hands on algorithms of machine learning and also provide us the backup classes. The access they provide is very helpful to listen the classes repeatedly. They provide good placement assistance. Thanks social ACTE team for your support and guidance.

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    Data Science Course FAQs

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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 training has been built around Real Time Implementation
    • You Get Hands-on Experience with Industry Projects, Hackathons & lab sessions which will help you to Build your Project Portfolio
    • GitHub repository and Showcase to Recruiters in Interviews & Get Placed
    All the instructors at ACTE are practitioners from the Industry with minimum 9-12 yrs of relevant IT experience. They are subject matter experts and are trained by ACTE for providing an awesome learning experience.
    No worries. ACTE assure that no one misses single lectures topics. We will reschedule the classes as per your convenience within the stipulated course duration with all such possibilities. If required you can even attend that topic with any other batches.
    We offer this course in “Class Room, One to One Training, Fast Track, Customized Training & Online Training” mode. Through this way you won’t mess anything in your real-life schedule.

    Why Should I Learn Data Science Course At ACTE?

    • Data Science Course in ACTE is designed & conducted by Data Science experts with 10+ years of experience in the Data Science domain
    • Only institution in India with the right blend of theory & practical sessions
    • In-depth Course coverage for 60+ Hours
    • More than 50,000+ students trust ACTE
    • Affordable fees keeping students and IT working professionals in mind
    • Course timings designed to suit working professionals and students
    • Interview tips and training
    • Resume building support
    • Real-time projects and case studies
    Yes We Provide Lifetime Access for Student’s Portal Study Materials, Videos & Top MNC Interview Question.
    You will receive ACTE globally recognized course completion certification Along with National Institute of Education (NIE), Singapore.
    We have been in the training field for close to a decade now. We set up our operations in the year 2009 by a group of IT veterans to offer world class IT training & we have trained over 50,000+ aspirants to well-employed IT professionals in various IT companies.
    We at ACTE believe in giving individual attention to students so that they will be in a position to clarify all the doubts that arise in complex and difficult topics. Therefore, we restrict the size of each Data Science batch to 5 or 6 members
    Our courseware is designed to give a hands-on approach to the students in Data Science. The course is made up of theoretical classes that teach the basics of each module followed by high-intensity practical sessions reflecting the current challenges and needs of the industry that will demand the students’ time and commitment.
    You can contact our support number at +91 93800 99996 / Directly can do by's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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