Data Science Training In Chennai | Placement Support ⭐ | Updated 2025
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Data Science Training in Chennai

6987 Ratings

Rated #1 Recoginized as the No.1 Institute for Data Science Course in Chennai

Rated as the Top Data Science Training Institute in Chennai, We Empower Students and Professionals to Master Core Data Science Concepts and Secure High-Paying Careers as Data Scientists.

Our Data Science Training and Placement are designed with real-world applications in mind, preparing students for success in the workplace. Our knowledgeable professors ensure that students receive top-notch instruction, Data Science practical experience, and personalized guidance.

  • Highly Rated Data Science Training in Chennai.
  • The Best Curriculum at an Affordable Cost With 100% Placement.
  • More Than 11462+ Trained Learners and 380+ Employing Clients.
  • Get the Advantage of Endless Chances To Interview With Leading MNCs.
  • Join in Interesting Workshops That Will Speed Up Your Data Science Job Offers.
  • Gain Knowledge From Data Science certification Experts With 8+ Years of Experience.

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  • Case Studies and Projects 8+

  • Hours of Training 45+

  • Placement Assurance 100%

  • Expert Support 24/7

  • Support & Access Lifetime

  • Certification Yes

  • Skill Level All

  • Language All

Advance Your Career with Our Data Science Training in Chennai

  • Our Data Science Training equips professionals with the skills to analyze, interpret, and derive insights from data. It focuses on machine learning, data visualization, and predictive analytics.
  • Key topics include data wrangling, statistical modeling, programming in Python/R, and big data tools, providing participants with a comprehensive understanding of data science methodologies.
  • We offer flexible learning options, including self-paced courses, instructor-led training (ILT), virtual ILT, and on-demand modules to accommodate diverse schedules and preferences.
  • Our certified and experienced instructors deliver hands-on, practical sessions to prepare participants for real-world data challenges and effective decision-making.
  • Certification from our Data Science Training in Chennai empowers trainees with the expertise needed to excel as data scientists, data analysts, and machine learning engineers.
  • Our program includes career development support, such as resume building, interview coaching, job placement assistance, and networking opportunities to boost trainees' professional prospects.
  • Upon completing our Data Science Training, students will be able to analyze complex datasets, build predictive models, and implement data-driven solutions to drive business success.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

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+91-7669 100 251
Other Categories Placements
  • Non-IT to IT (Career Transition) 2371+
  • Diploma Candidates3001+
  • Non-Engineering Students (Arts & Science)3419+
  • Engineering Students3571+
  • CTC Greater than 5 LPA4542+
  • Academic Percentage Less than 60%5583+
  • Career Break / Gap Students2588+
Top Placement Record

Upcoming Batches

16-June-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM

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

18-June-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM

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

21-June-2025
Saturday

Weekend Regular

(10:00 AM - 01:30 PM)

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

22-June-2025
Sunday

Weekend Fasttrack

(09:00 AM - 02:00 PM)

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

    Hear it from our Graduate

    Best Practices on Job Oriented Modules Covered in Data Science in Delhi

    Course Objectives

    • Strengthens your skills in data analysis and modeling
    • Expands career opportunities in data science and analytics
    • Provides globally recognized certifications
    • Enhances expertise in data tools, techniques, and methodologies
    • Facilitates networking with industry professionals and experts

      A basic understanding of statistics, programming, and data management is recommended. Familiarity with analytical tools and concepts can be helpful, but prior experience in data science is not mandatory.

    • Data Scientist
    • Data Analyst
    • Machine Learning Engineer
    • Business Intelligence Analyst
    • Data Engineer
    • AI Specialist

    The course offers hands-on experience in data cleaning, exploratory analysis, building predictive models, and creating data visualizations. Participants work on real-world projects that include big data, machine learning, and statistical modeling.

      • Introduction to Data Science and Analytics
      • Programming for Data Science (Python, R)
      • Statistical Analysis and Machine Learning
      • Data Visualization and Reporting
      • Big Data Tools and Technologies
      • Model Deployment and Optimization
      • Basic knowledge of statistics and programming
      • Familiarity with data analysis tools and methodologies
      • Strong analytical and problem-solving skills
      • Proficiency in data visualization and management
      • Attention to detail and critical thinking

    Yes, beginners can effectively learn Data Science with this course. It is designed with step-by-step lessons and practical projects to help build a strong foundation in data science principles and techniques.

    What tools are commonly used in Data Science training?

    • Python, R, and SQL for data analysis
    • Tableau and Power BI for data visualization
    • TensorFlow and sci-kit-learn for machine learning
    • Hadoop and Spark for big data processing
    • Statistical analysis tools

    How relevant is Data Science in today’s industry?

      Data Science is highly relevant as organizations increasingly rely on data-driven strategies for decision-making. Skilled professionals in data analysis and machine learning are in high demand across industries.

    What is the career potential for Data Science professionals?

      The demand for data science professionals continues to grow, with opportunities in areas such as predictive modeling, data analytics, and AI. Staying updated with emerging tools and technologies enhances career prospects significantly.

    Is learning Data Science difficult?

      While Data Science can be challenging, it is achievable with consistent effort and a willingness to learn. A basic understanding of programming and statistics, along with hands-on practice, makes the learning process manageable for beginners and professionals alike.

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    Overview of Data Science Training

    Data Science training is vital for leveraging data to drive informed decision-making in professional settings. It provides individuals with the knowledge and skills to analyze data, create predictive models, and apply machine learning and statistical techniques to solve complex challenges. The training covers advanced topics in data processing, visualization, and analytics, ensuring alignment with industry standards. As data becomes increasingly central to innovation and strategy, Data Science training is crucial for building expertise in managing and interpreting data, enabling organizations to strengthen decision-making and maintain a competitive advantage.

     

    Additional Info

    The Future Scope and Applications of Data Science

    • Rising Demand: The increasing sophistication of cyberattacks has led to a growing need for ethical hackers to protect critical systems. Companies rely more on ethical hackers to bolster their cybersecurity frameworks.
    • Career Opportunities: Ethical hacking skills open up career paths such as Penetration testers, Security Consultant, and Cybersecurity Analyst, offering high-paying roles and the chance to work with the latest technologies.
    • Technological Progress: Ethical hacking evolves with advancements in cybersecurity tools, allowing professionals to stay ahead of complex cyber threats. Ongoing training ensures experts remain up-to-date with the latest techniques.
    • Critical Infrastructure Protection: Ethical hackers play a vital role in securing sectors like healthcare, energy, and finance. By uncovering vulnerabilities, they help safeguard essential systems and services from attacks.
    • IoT Security: The rapid growth of IoT devices requires Ethical Hacking to address security gaps in interconnected systems. It ensures these devices remain secure, protecting personal and enterprise data.
    • AI and Machine Learning Integration: Ethical hacking uses AI and machine learning to enhance threat detection and prevention, enabling hackers to predict vulnerabilities and respond proactively.
    • Critical Infrastructure Protection: Ethical hacking is vital for protecting critical infrastructure in healthcare, finance, and energy, preventing disruptions by securing systems against cyber threats.
    • Regulatory Compliance: Organizations rely on ethical hacking to ensure compliance with data protection and cybersecurity regulations, meeting legal requirements and maintaining customer and partner trust.

    The Functions and Importance of Data Science

    • Data Collection: Data science involves gathering large volumes of structured and unstructured data from various sources. This data serves as the foundation for analysis, insights, and decision-making.
    • Data Cleaning: A key function of data science is cleaning and preprocessing data to eliminate errors, inconsistencies, and outliers. This ensures the accuracy and reliability of the data for further analysis.
    • Data Analysis: Data scientists analyze data using statistical methods and algorithms to identify patterns, trends, and relationships. This analysis helps organizations make informed decisions based on data-driven insights.
    • Predictive Modeling: Data science employs predictive models to forecast future trends, behaviors, or outcomes. These models help businesses anticipate challenges, and opportunities, and make proactive decisions.
    • Machine Learning: Machine learning algorithms allow data scientists to build models that can automatically learn and improve from data. This enables automation of tasks like classification, regression, and anomaly detection.
    • Data Visualization: Data science involves presenting complex data in an understandable visual format, such as charts and graphs. This helps stakeholders easily interpret findings and make data-driven decisions.
    • Big Data Management: With the increasing volume of data, data science plays a critical role in managing big data efficiently. It involves using advanced tools and technologies to store, process, and analyze massive datasets.
    • Business Strategy Optimization: By analyzing data trends and insights, data science helps organizations optimize business strategies. It enables smarter decision-making, improving overall efficiency and profitability.

    Top Data Science Tools in Use Today

    • Python: Python is a widely-used programming language in data science, known for its simplicity and powerful libraries like Pandas, NumPy, and Scikit-learn, which assist in data analysis and machine learning tasks.
    • R: R is a specialized programming language for statistical computing and data analysis, offering extensive packages for modeling, machine learning, and data visualization.
    • Apache Hadoop: Hadoop is an open-source framework designed for processing and storing large-scale data across distributed systems, making it ideal for big data analytics.
    • Tableau: Tableau is a leading data visualization tool that helps data scientists create dynamic, interactive dashboards, making complex data insights accessible to a wider audience.
    • SQL: SQL is the standard language for querying and managing relational databases, essential for data extraction, data manipulation, and complex queries in data science.
    • TensorFlow: TensorFlow is an open-source machine learning framework developed by Google, widely used for developing and deploying deep learning models at scale.
    • Apache Spark: Spark is a fast and efficient engine for big data analytics, offering support for real-time data processing, machine learning, and stream analytics.
    • Jupyter Notebooks: Jupyter Notebooks is an interactive environment that allows data scientists to write code, visualize data, and document their processes, enhancing collaboration and experimentation.

    Advantages of Data Science Training

    • Career Growth: Data Science training opens doors to high-demand roles like Data Analyst, Data Scientist, and Machine Learning Engineer, offering strong career prospects and growth opportunities.
    • Hands-on Experience: Training provides practical experience with real-world data sets and tools, helping learners build skills that are directly applicable to industry challenges.
    • Increased Earning Potential: Data Science professionals are among the highest-paid in the tech industry, and specialized training can significantly boost earning potential.
    • Problem-Solving Skills: Data Science training sharpens your ability to analyze complex data, identify trends, and develop actionable insights to solve business problems effectively.
    • Technical Proficiency: The training covers essential data analysis and visualization tools such as Python, R, SQL, and Tableau, making you proficient in industry-standard technologies.
    • Critical Thinking Development: Data Science training enhances your analytical thinking, enabling you to approach problems from multiple perspectives and make data-driven decisions.
    • Business Understanding: Through training, you'll gain insights into how data science integrates with business strategy, enabling you to contribute meaningfully to business growth.
    • Industry Relevance: The skills learned in Data Science training are highly relevant to industries ranging from healthcare and finance to retail and technology, providing broad career opportunities.

    Essential Skills for Data Science Professionals

    • Statistical Analysis: Understanding statistical methods allows data scientists to identify patterns and relationships within data. This is essential for making informed predictions and business decisions.
    • Programming Languages: Proficiency in languages like Python, R, and SQL is critical for data manipulation and analysis. These tools enable professionals to automate tasks and process data effectively.
    • Machine Learning: Knowledge of machine learning techniques helps in creating predictive models that improve decision-making. It enables data scientists to develop algorithms for various applications.
    • Data Visualization: Mastering data visualization tools such as Tableau and Matplotlib is essential for presenting insights. These tools help in translating complex data into understandable visual formats.
    • Data Wrangling: Data wrangling skills involve cleaning, transforming, and organizing raw data. This process ensures that the data is structured and ready for analysis.
    • Big Data Technologies: Familiarity with platforms like Hadoop and Spark is key for handling large datasets. These technologies help in managing and analyzing vast amounts of data efficiently.
    • Problem-solving: Data scientists need strong problem-solving abilities to identify trends and make data-driven decisions. This skill is crucial for solving complex business challenges and optimizing processes.
    • Domain Knowledge: Having domain expertise allows data scientists to apply data science techniques effectively within specific industries. It helps in generating more relevant and actionable insights.
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    Key Attribute

    ACTE Chennai offers Data Science Training in more than 27+ branches with expert trainers. Here are the key features,

    • Short Span Training Program
    • Effective Placement Support
    • Qualified Industry experienced Instructors
    • Deliver a Free Demo Classes
    • Achieved 500+ Candidate Batches
    • Certification Support

    Authorized Partners

    ACTE TRAINING INSTITUTE PVT LTD is the unique Authorised Oracle Partner, Authorised Microsoft Partner, Authorised Pearson Vue Exam Center, Authorised PSI Exam Center, Authorised Partner Of AWS .

    Curriculum

    Syllabus of Data Science Course in Chennai
    Module 1: Data Science Basics
    • Introduction to Data Science
    • Significance of Data Science
    • R Programming basics
    Module 2: Python Fundamentals
    • Python Introduction
    • Indentations in Python
    • Python data types and operators
    • Python Functions
    Module 3: Data Structures and Data Manipulation
    • Data Structures Overview
    • Identifying the Data Structures
    • Allocating values to the Data Structures
    • Data Manipulation Significance
    • Dplyr Package and performing different data manipulation operations
    Module 4: Data visualization
    • Introduction to Data Visualisation
    • Various kinds of graphs, Graphics grammar
    • Ggplot2 package
    • Multivariant analysis by using geom_boxplot
    • Univariant analysis
    • Histogram, barplot, multivariate distribution, and density plot
    • Bar plots for the categorical variables through geop_bar() and the theme() layer
    Module 5: Statistics
    • Statistics Importance
    • Statistics classification, Statistical terminology
    • Data types, Probability types, measures of speed, and central tendency
    • Covariance and Correlation, Binary and Normal distribution
    • Data Sampling, Confidence, and Significance levels
    • Hypothesis Test and Parametric testing
    Module 6: Introduction to Machine Learning
    • Machine Learning Fundamentals
    • Supervised Learning, Classification in Supervised Learning
    • Linear Regression and mathematical concepts related to linear regression
    • Classification Algorithms, Ensemble Learning techniques
    Module 7: Logistic Regression
    • Logistic Regression Introduction
    • Logistic vs Linear Regression, Poisson Regression
    • Bivariate Logistic Regression, math related to logistic regression
    • Multivariate Logistic Regression
    • Building Logistic Models
    • False and true positive rate
    • Real-time applications of Logistic Regression
    Module 8: Random Forest and Decision Trees
    • Classification Techniques
    • Decision Tree Induction Algorithm
    • Implementation of Random Forest in R
    • Differences between classification tree and regression tree
    • Naive Bayes, SVM
    • Entropy, Gini Index, Information Gain
    Module 9: Unsupervised learning
    • Clustering, K-means clustering, Canopy Clustering, and Hierarchical Clustering
    • Unsupervised learning, Clustering algorithm, K-means clustering algorithm
    • K-means theoretical concepts, k-means process flow, and K-means implementation
    • Implementing Historical Clustering in R
    • PCA(Principal Component Analysis) Implementation in R
    Module 10: Denial-of-Service
    • DoS/DDoS Concepts
    • Botnets
    • DoS/DDoS Attack Techniques
    • DDoS Case Study
    • DoS/DDoS Countermeasures
    Module 11: Natural Language Processing
    • Natural language processing and Text mining basics
    • Significance and use-cases of text mining
    • NPL working with text mining, Language Toolkit(NLTK)
    • Text Mining: pre-processing, text-classification and cleaning
    Module 12: Mathematics for Data Science
    • Numpy Basics
    • Numpy Mathematical Functions
    • Probability Basics and Notation
    • Correlation and Regression
    • Joint Probabilities
    • Bayes Theorem
    • Conditional Probability, sum rule, and product rule
    Module 13: Scientific Computing through Scipy
    • Scipy Introduction and characteristics
    • Integrate, Cluster, Signal, Fftpack, and Bayes Theorem
    Module 14: Python Integration with Spark
    • Pyspark basics
    • Uses and Need of pyspark
    • Pyspark installation
    • Advantages of pyspark over MapReduce
    • Pyspark applications
    Module 15: Deep Learning and Artificial Intelligence
    • Machine Learning effect on Artificial Intelligence
    • Deep Learning Basics, Working of Deep Learning
    • Regression and Classification in the Supervised Learning
    • Association and Clustering in unsupervised learning
    • Basics of Artificial Intelligence and Neural Networks
    • Supervised Learning in Neural Networks, multi-layer network
    • Deep Neural Networks, Convolutional Neural Networks
    • Reinforcement Learning
    • Recurrent Neural Networks, Deep learning graphics processing unit
    • Deep Learning Applications, Time series modeling
    Module 16: Keras and TensorFlow API
    • Tensorflow Basics and Tensorflow open-source libraries
    • Deep Learning Models and Tensor Processing Unit(TPU)
    • Graph Visualisation, keras
    • Keras neural-network
    • Define and Composing multi-complex output models through Keras
    • Batch normalization, Functional and Sequential composition
    • Implementing Keras with tensorboard
    • Implementing neural networks through TensorFlow API
    Module 17: Restricted Boltzmann Machine and Autoencoders
    • Basics of Autoencoders and rbm
    • Implementing RBM for the deep neural networks
    • Autoencoders features and applications
    Module 18: Big Data Hadoop and Spark
    • Big Data and Hadoop Basics
    • Hadoop Architecture, HDFS
    • MapReduce Framework and Pig
    • Hive and HBase
    • Basics of Scala and Functional Programming
    • Kafka basics, Kafka Architecture
    • Kafka cluster and Integrating Kafka with Flume
    • Introduction to Spark
    • Spark RDD Operations, writing spark programs
    • Spark Transformations, Spark streaming introduction
    • Spark streaming Architecture, Spark Streaming Features
    • Structured streaming Architecture, Dstreams, and Spark Graphx
    Module 19: Tableau
    • Data Visualisation Basics
    • Data Visualisation Applications
    • Tableau Installation and Interface
    • Tableau Data Types, Data Preparation
    • Tableau Architecture
    • Getting Started with Tableau
    • Creating sets, Metadata and Data Blending
    • Arranging visual and data analytics
    • Mapping, Expressions, and Calculations
    • Parameters and Tableau prep
    • Stories, Dashboards, and Filters
    • Graphs, charts
    • Integrating Tableau with Hadoop and R
    Module 20: MongoDB
    • MongoDB and NoSQL Basics
    • MongoDB Installation
    • Significance of NoSQL
    • CRUD Operations
    • Data Modeling and Management
    • Data Indexing and Administration
    • Data Aggregation Schema
    • MongoDB Security
    • Collaborating with Unstructured Data
    Module 21: SAS Basics
    • SAS Enterprise Guide
    • SAS functions and Operators
    • SAS Data Sets compilation and creation
    • SAS Procedures
    • SAS Graphs
    • SAS Macros
    • PROC SQL
    • Advance SAS
    Module 22: MS Excel
    • Entering Data
    • Logical Functions
    • Conditional Formatting
    • Validation, Excel formulas
    • Data sorting, Data Filtering, Pivot Tables
    • Creating charts, Charting techniques
    • File and Data security in excel
    • VBA macros, VBA IF condition, and VBA loops
    • VBA IF condition, For loop
    • VBA Debugging and Messaging
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    Need customized curriculum?

    Hands-on Real Time Projects for Data Science Training in Chennai

    Project 1
    Customer Churn Prediction Model

    Build a predictive model to identify customers who are likely to leave a service using historical data, customer demographics, and usage patterns, with the help of machine learning algorithms like decision trees or logistic regression.

    Project 2
    Sentiment Analysis on Social Media Data

    Develop a sentiment analysis model to classify social media posts or reviews as positive, negative, or neutral. Use Natural Language Processing (NLP) techniques along with machine learning algorithms to analyze the sentiment of text data.

    Kickstart Your Career with Our Data Science Job Placement Service

    • Our Data Science placement service fosters strong connections with leading companies, ensuring students secure positions in top organizations within the data science field.
    • We equip candidates with comprehensive mock interviews and practice exams, enhancing their confidence and preparedness for competitive job interviews.
    • Our placement portal offers easy access to interview schedules and timely updates on job openings, keeping students informed about new career opportunities.
    • Our Data Science placement team takes the time to understand each student's career goals and matches them with roles that align with their aspirations in data science.
    • We collaborate with a wide range of companies, from startups to global corporations, providing diverse placement opportunities across various industries and company sizes.
    • Our institute boasts a strong track record of successfully placing students in renowned organizations like Google, Amazon, and Microsoft, showcasing our commitment to exceptional placement services.
    • Our extensive industry connections reflect our dedication to offering valuable placement opportunities and supporting the career advancement of students in the Data Science field.

    Obtain an Industry-Accredited Data Science Certification

    Obtaining our Data Science Certification significantly boosts your qualifications and broadens your career opportunities in the data science domain. It showcases your proficiency in data analysis, predictive modeling, and applying advanced analytical techniques. This highly respected certification improves your appeal to employers and sets you up for success in roles such as data analyst, machine learning engineer, and business intelligence expert, helping you advance in your career and access valuable job prospects.

    • Validates Your Expertise
    • Expands Career Opportunities
    • Increases Marketability
    • Enhances Analytical and Technical Skills
    • Gains Industry Recognition
    While a Data Science certification doesn't guarantee job placement, it significantly boosts your chances by highlighting your ability to analyze data and create actionable insights. This certification increases your attractiveness to employers in the data science field, leading to better job opportunities.
    To earn a Data Science certification, you need a solid foundation in programming, statistics, and data analysis. Familiarity with tools like Python, R, SQL, and machine learning algorithms is also essential.
    • Certified Data Scientist (CDS)
    • Certified Analytics Professional (CAP)
    • Google Professional Data Engineer
    • IBM Data Science Professional Certificate
    • Data Scientist
    • Data Analyst
    • Machine Learning Engineer
    • Business Intelligence Analyst
    • Data Engineer

    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

    Gain Expert Insights from Our Data Science Trainers

    • Our Data Science trainers are seasoned professionals with advanced certifications and hands-on experience from top global companies, ensuring high-quality, practical instruction.
    • They bring valuable insights from industry leaders and have worked with major organizations like Google, Amazon, and Microsoft, enriching the learning experience with real-world case studies.
    • Our Data Science trainers foster engaging, interactive sessions with customized lessons, catering to various learning styles to ensure effective and enjoyable training.
    • They excel at simplifying complex data science concepts, making them accessible to participants at all skill levels. Their approach encourages active participation and ensures thorough comprehension.
    • Our Data Science trainers stay current with the latest data science trends and technologies, regularly updating course materials to reflect evolving industry standards and best practices.
    • The training focuses on hands-on experience, equipping participants with the skills necessary to solve real-world data science challenges and excel in data-driven roles.
    • Our trainers offer valuable mentorship, leveraging their industry experience to provide actionable guidance and help participants master data science concepts for career success.

    Data Science Course FAQs

    Who are the course instructors, and how do they get selected?

    At ACTE, all trainers are experienced industry professionals with a minimum of 10-12 years of relevant IT experience. They work in live corporate environments, ensuring the training is rooted in practical, up-to-date industry knowledge. Chosen for their expertise, teaching skills, and real-world experience, they deliver high-quality, hands-on training tailored to industry needs.
    Of course, ACTE offers practice tests designed to help learners assess and reinforce their knowledge and skills in the relevant domain. These tests provide an effective way to evaluate progress and prepare for real-world applications.
    • ACTE is the Legend in offering placement to the students. Please visit our Placed Student list on our website.
    • We have solid working relationships with more than 700+ of the world's leading multinational corporations, including SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, ZOHO, TCS, IBM, and more.
    • More than 3500+ students placed last year in India & Globally. Our Placement Cell support you till you get placed in a better MNC.
    • Please Visit Your Student Portal. Here, the FREE Lifetime Online Student Portal helps you to access Job Openings, Study Materials, Videos, Recorded sections & Top MNC Interview Questions.
    Yes, we offer job assistance upon course completion:
    • Practical Experience
    • Hands-On Projects
    • Resume Preparation
    • Aptitude and Technical skills
    • Mock Interview
    • Group Discussion
    To enroll in ACTE's Data Science course, visit their website, navigate to the course page, and click Enroll Now. Fill out the form, choose your preferred batch timing, and start your Data Science journey today!
    The Data Science Master Program Training at ACTE provides a solid foundation in data science, covering data analysis, machine learning, and statistical modeling. You'll gain hands-on experience with Python, R, and tools for data manipulation and visualization. The program includes practical applications of data cleaning, analysis, and machine learning algorithms, along with real-world projects to solve complex data challenges.
    The ACTE certification validates abilities and knowledge in particular domains, which is valuable for career advancement. However, depending on employer preferences, industry norms, and how applicable the accreditation is for specific roles, its significance and recognition may differ.
    • Basic Programming Skills
    • Mathematics and Statistics
    • Data Handling and Databases
    • Understanding of Machine Learning
    • Problem-Solving and Analytical Thinking
    Yes, many training programs require participants to work on real projects. This provides practical experience, a deeper understanding of real-world scenarios, and the development of practical skills, improving employability and readiness for professional roles.
    If you are unable to attend a class at ACTE, you can view recorded sessions or request make-up lessons. Additionally, resources and training materials are provided to help you compensate for missed content.
    Our course is easily manageable alongside your real-life commitments because it is available in the classroom, one-on-one, fast-track, customized, and online training modes.
    Enroll in ACTE’s best Data Science course to benefit from comprehensive training, an industry-aligned curriculum, experienced instructors, real-world projects, certification, job placement support, and a supportive learning environment to help ensure your success in the field.
    Yes, the Data Science course provides hands-on practice, real-world projects, and interactive discussions. Mock interviews and group discussions are included to improve your abilities and preparedness for the workforce. These exercises ensure you are ready to take on technical problems and perform well in team-oriented settings.
    Yes, we give students lifetime access to the study materials, videos, and top MNC interview questions on the Student’s Portal.
    We offer recorded video lessons for the training. After you complete the payment and enroll in the course, you will gain access to the pre-recorded sessions through our course platform, allowing you to watch them at your own pace.
    The salary range for a Data Science programmer is between 6 lakhs and 15 lakhs per year.
    Learning Data Science is engaging, provided you have the proper resources and a systematic approach. Once you grasp the basics, things become more intuitive, and real-world applications make the process interesting and fun.
    • Data Collection
    • Data Cleaning
    • Data Analysis
    • Data Visualization
    Data Science is a fantastic field for freshers. It offers many opportunities to learn and grow. The demand for skilled professionals is presently high across various industries. With the right training and resources, freshers can quickly acquire programming, statistics, and machine learning skills. The field also constantly changes, bringing exciting challenges and opportunities to work on innovative projects.
    Data Science affects many sectors, including healthcare, finance, and e-commerce. It encourages innovation through data-driven insights, playing a critical role in predictive analytics, customer behavior understanding, and optimization strategy development.
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          Job Opportunities in Data Science

          More Than 35% Of Developers Prefer Data Science. Data Science Is The Most Popular And In-Demand Programming Language In The Tech World.

          You can Work as a
          Data Analyst
          Data Scientist
          Business Analyst
          Machine Learning Engineer
          Deep Learning Engineer
          Data Engineer
          Upcoming In-Demand Jobs
          AI Engineer
          Cybersecurity Analyst
          Quantitative Analyst
          Data Science Manager
          Data Architect
          Salary in Data Science

          Reach Our Placement Officer

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