Expert Data Science Course in Hyderabad with Placement | Updated 2025
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Data Science Course in Hyderabad

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Rated #1 Recognized as the No.1 Institute for Data Science Course in Hyderabad

Our extensive, captivating, and practical Data Science course in Hyderabad teaches you machine learning, statistical analysis, data science, advanced data approaches, data visualization and predictive modeling.

The course covers deep learning, machine learning techniques, data visualization, data preprocessing, and fundamental data science ideas. Working on real-world projects will provide you practical experience. Get your certification, finish the Data Science course, and take advantage of Data Science placement to advance career.

  • Join our Data Science training register now!
  • Join 350+ hiring companies and 13,898+ successful graduates.
  • Get hands-on experience with real-world data science projects.
  • Boost your career prospects with a professional data science certification.
  • Explore exciting career opportunities in data analysis, AI, and machine learning.
  • Access a curriculum that is industry-standard and reasonably priced, with job assistance.

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Fee INR 18000

INR 14000

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Training

Enroll Now View Syllabus

  • 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

Elevate Your Career with Our Data Science Training

  • Our Data Science training is meticulously designed to impart a comprehensive understanding of Data Science principles, practices, and real-world applications, essential in today's data-driven industries.
  • With our Data Science training, students can pursue rewarding careers in data analysis, machine learning, and various data science-related fields, equipped with highly sought-after skills across diverse industries.
  • Our Data Science training prioritizes hands-on learning through immersive exercises, practical projects, and industry-standard case studies, ensuring practical skill development aligned with current industry demands.
  • Through dedicated interview preparation sessions led by our Placement Support Team, students gain confidence and proficiency to excel in job interviews and secure promising opportunities in Data Science roles.
  • Advanced Data Science subjects include predictive modeling approaches, machine learning algorithms, and cutting-edge big data technologies like Apache Spark and Hadoop.
  • Data Science training offers a streamlined pathway for individuals aspiring to thrive in the dynamic realm of data analysis and machine learning. It provides essential skills and expertise tailored for success in the Data Science landscape.
  • 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

28-Apr-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

30-Apr-2025
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

03-May-2025
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

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

04-May-2025
Sat,Sun

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 Tools Covered in Data Science Course in Hyderabad

    Course Objectives

    • Collaborative Data Analysis
    • Automation Proficiency
    • Continuous Improvement
    • Scalability Enhancement

    Absolutely, Data Science courses cater to beginners, providing foundational knowledge and skills necessary to enter the field. These courses cover fundamental concepts, tools, and practices, making them accessible to individuals with little to no prior experience in Data Science.

    • Data Analysis and Visualization
    • Machine Learning Algorithms
    • Statistical Modeling
    • Big Data Technologies like Apache Spark
    • Data Preprocessing Techniques

    Yes, students in Data Science Training engage in real-world projects to apply their skills and gain practical experience. These projects simulate industry scenarios, allowing students to solve common challenges encountered in Data Science practices.

    Boost your data analysis speed and precision while honing automation skills for streamlined data processing. Acquire in-demand Data Science expertise to broaden your career horizons in the fast-paced, data-driven industry. Stay at the forefront of this rapidly evolving field to seize emerging opportunities and maintain a competitive edge.

    Indeed, there is a high demand for Data Science training due to the increasing reliance on data-driven decision-making across industries. Data Science training programs are sought after by individuals and organizations aiming to enhance their skills and stay competitive in the data-centric landscape.

    • Data Scientist
    • Machine Learning Engineer
    • Data Analyst
    • Business Intelligence Analyst
    • Big Data Engineer

    What is the future scope of Data Science courses?

    • Continued Industry Relevance
    • Increased Adoption Across Sectors
    • Evolution of Tools and Technologies
    • Emphasis on Data Security and Privacy
    • Utilization in Hybrid and Multi-Cloud Environments

    List the tools used in Data Science course?

    • Python Programming Language
    • R Programming Language
    • Jupyter Notebooks
    • Apache Spark
    • Tableau for Data Visualization

    What Prerequisites are Required for Learning Data Science?

    • Basic understanding of statistics and mathematics.
    • Familiarity with programming languages like Python or R.
    • Understanding of data manipulation techniques.
    • Experience with data visualization tools.

    Does Data Science need Coding?

    Yes, Data Science often involves coding for tasks such as data preprocessing, algorithm implementation, and building predictive models. While not mandatory for all roles, coding skills greatly enhance a Data Scientist's ability to manipulate and analyze data effectively.

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    Overview of Data Science

    Data Science is an interdisciplinary field that utilizes scientific methods, algorithms, and systems to extract insights and knowledge from structured and unstructured data. It encompasses various techniques such as data mining, machine learning, and statistical analysis to uncover patterns, trends, and correlations. Data Scientists leverage programming languages like Python and R, along with tools like TensorFlow and Apache Spark, to analyze and interpret data. The insights derived from Data Science are utilized to inform business decisions, improve processes, and drive innovation across industries. With its increasing importance in today's digital age, Data Science offers vast career opportunities and plays a crucial role in shaping the future of technology and business.

    Additional Information

    Career Scope of Data Science

    • Diverse Industries: Data Scientists are in demand across industries such as healthcare, finance, retail, marketing, and technology, providing ample career options.
    • High Demand: With the increasing reliance on data-driven decision-making, the demand for Data Scientists is expected to continue rising, ensuring a steady stream of job opportunities.
    • Lucrative Salaries: Data Science roles command competitive salaries due to the specialized skills and expertise required, offering attractive compensation packages.
    • Career Advancement: Data Science offers opportunities for career growth and advancement, with pathways to senior and leadership positions such as Data Science Manager or Chief Data Officer.
    • Innovation: Data Scientists have the opportunity to work on cutting-edge projects and contribute to innovation through the development of predictive models, machine learning algorithms, and data-driven insights.
    • Global Opportunities: Data science abilities are in great demand worldwide, with several chances for international career progression and mobility. With a strong demand across sectors, Data Science experts may explore a wide range of employment marketplaces and pursue fulfilling careers all around the world.
    • Interdisciplinary Skills: Data Science professionals develop a diverse skill set encompassing statistics, programming, machine learning, and domain expertise, making them versatile and adaptable to various roles and industries.
    • Impactful Work: Data Scientists have the opportunity to make a significant impact by solving complex problems, driving business growth, and improving decision-making processes through data analysis and insights.

    Roles and Responsibilities of the Data Science

    • Data Scientist: Data scientists are responsible for analyzing large volumes of data to extract actionable insights, developing predictive models using machine learning techniques, and effectively communicating their findings to stakeholders.
    • Machine Learning Engineer: Machine learning engineers focus on designing and optimizing machine learning algorithms and models, deploying these models into production environments, and collaborating closely with data scientists to ensure successful integration.
    • Data Analyst: Data analysts clean and explore data to identify trends and patterns, create reports and dashboards to present their findings, and support decision-making processes within organizations based on their data-driven insights.
    • Business Intelligence Analyst: Business intelligence analysts analyze business data to generate actionable insights, create visually appealing data visualizations and dashboards, and identify opportunities for process improvement and optimization.
    • Big Data Engineer: Big data engineers design, build, and maintain large-scale data processing systems, develop and optimize data pipelines for efficient data ingestion and processing, and ensure the security and integrity of data within these systems.
    • Data Engineer: Data engineers build and manage data infrastructure, develop ETL (Extract, Transform, Load) processes to prepare data for analysis, and collaborate closely with data scientists and analysts to ensure the availability and quality of data.

    Tools Used for Data Science

    • Python: A versatile programming language widely used for data analysis, machine learning, and scientific computing due to its rich ecosystem of libraries like NumPy, Pandas, and Scikit-learn.
    • R: A programming language and environment specifically designed for statistical analysis and visualization, commonly used in academia and research for data analysis tasks.
    • Jupyter Notebook: An open-source web application that allows users to create and share documents containing live code, equations, visualizations, and narrative text, making it an ideal tool for interactive data analysis and exploration.
    • SQL: Structured Query Language (SQL) is essential for working with relational databases, allowing data scientists to query, manipulate, and manage data stored in databases efficiently.
    • TensorFlow: An open-source machine learning framework developed by Google for building and training deep learning models, widely used for tasks like image classification, natural language processing, and reinforcement learning.
    • Scikit-learn: A simple and efficient library for machine learning in Python, providing tools for data preprocessing, model selection, and evaluation, making it suitable for both beginners and experienced data scientists.
    • Apache Spark: A fast and general-purpose cluster computing system for big data processing, offering APIs in Java, Scala, Python, and R, commonly used for large-scale data processing, machine learning, and stream processing tasks.
    • Tableau: A powerful data visualization tool that allows users to create interactive and shareable dashboards, reports, and visualizations, enabling data scientists to communicate their findings effectively to stakeholders.
    • Docker: A containerization platform that allows developers to package and distribute applications and their dependencies in isolated environments, making it easier to deploy and manage data science projects across different environments.
    • Git: A distributed version control system used for tracking changes in source code during software development, essential for collaboration and version management in data science projects.

    Organizational Benefits of the Data Science

    • Data-Driven Decision Making: Equipping employees with data science skills enables organizations to make informed decisions based on data analysis, leading to improved efficiency and effectiveness in various business processes.
    • Increased Competitiveness: By leveraging data science techniques, organizations can gain insights into market trends, customer behavior, and competitor strategies, allowing them to stay ahead of the competition and seize new opportunities.
    • Improved Productivity: Data science empowers employees to automate repetitive tasks, streamline processes, and optimize workflows, leading to increased productivity and resource utilization across the organization.
    • Enhanced Customer Experience: By analyzing customer data, organizations can personalize products, services, and marketing campaigns to better meet the needs and preferences of their target audience, leading to higher customer satisfaction and loyalty.
    • Cost Reduction: Data science enables organizations to identify inefficiencies, eliminate waste, and optimize resource allocation, resulting in cost savings and improved financial performance.
    • Risk Management: Data science techniques such as predictive analytics help organizations identify and mitigate risks proactively, whether in financial investments, supply chain management, or cybersecurity, reducing potential losses and liabilities.
    • Innovation and Growth: Data science fosters innovation by uncovering insights, patterns, and trends that can lead to the development of new products, services, and business models, driving growth and market expansion.
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    Key Features

    ACTE Hyderabad 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 .

    Curriculum

    Syllabus of Data Science Course in Bangalore
    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
    Show More
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    Need customized curriculum?

    Get Hands-on Knowledge about Real-Time Data Science Projects

    Project 1
    Predictive Maintenance

    Predict equipment failures, optimize maintenance in manufacturing with ML.

    Project 2
    Customer Segmentation

    Use clustering for personalized marketing and improved customer engagement.

    Get Hired Quickly With Our Data Science Course Placement:

    • Begin your Data Science adventure with our professional mentors, who will provide individualized guidance based on your needs and goals. Our specialized staff will provide you with useful insights and support as you begin on your journey to learn Data Science.
    • Our Data Science placement ensures you to receive expert assistance in problem-solving, code reviews, and career guidance, empowering you to navigate the dynamic landscape of data analysis with confidence.
    • Enhance your confidence with customized guidance on technical interviews, behavioral assessments, and mock interviews tailored to Data Science-related roles.
    • To stand out in the competitive employment market, create compelling resumes that highlight your Data Science placement expertise and project successes. To boost your candidacy, showcase your skills with accuracy, focusing on project successes.
    • Gain access to unique Data Science opportunities with leading organizations like Google, Amazon, and Microsoft, among others. Our curriculum will provide you with the skills and knowledge required to flourish in the competitive profession of Data Science, opening the door to lucrative employment opportunities.
    • We nurture professional development through job expos, industry seminars, and networking events, connecting Data Science enthusiasts with industry leaders for valuable insights and opportunities.
    • Enhance your soft skills, including teamwork, communication, and presentation abilities, through our comprehensive training, essential for excelling in a Data Science career.

    Advance Your Career With Our Data Science Certification

    Our Data Science certification is globally recognized and esteemed by organizations worldwide. We cater to recent graduates, aspiring learners, and seasoned professionals seeking to enhance their skills. Renowned for its accreditation, the Data Science Certification significantly boosts the credibility of your resume, elevating your prospects of securing prestigious positions with leading organizations globally. To earn this esteemed certification, completion of hands-on training and assignments is mandatory, ensuring proficiency in practical application.

    Yes, achieving a Data Science certification can result in salary growth over time. It validates expertise, showcases commitment to professional development, and enhances competitiveness in salary negotiations.

    Data Science certification validates skills and expertise, enhancing employability and credibility. It demonstrates proficiency in Data Science practices and tools, opening doors to lucrative career opportunities.

    Yes it is highly beneficial:
    • Data Scientist
    • Machine Learning Engineer
    • Data Analyst
    • Business Intelligence Analyst
    • Big Data Engineer
    • AI Research Scientist
    • Increased job prospects
    • Demonstrated proficiency in Data Science
    • Enhanced credibility in the industry
    • Opportunities for career advancement
    • Potential for higher salary earnings

    Yes, obtaining a Data Science course certification significantly increases your chances of securing employment by validating essential skills and expertise. Certified individuals become highly desirable to employers in today's competitive job market, positioning them as top candidates for exciting career opportunities.

    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

    Embark on Your Journey with Our Expert Data Science Trainers

    • Our Data Science course boasts experienced trainers with a minimum of 7+ years of practical industry experience in Data Science methodologies, holding key positions in leading organizations.
    • Receive comprehensive guidance from our Data Science trainers, covering foundational to advanced concepts, ensuring a strong understanding of Data Science principles and techniques.
    • Our Data Science Trainers bring a wealth of expertise from renowned IT companies such as Accenture, IBM, Google, and Amazon, integrating real-world insights into the learning process.
    • Participate in an interactive learning environment where questions are encouraged, and our seasoned trainers provide continuous support and guidance throughout your Data Science training.
    • Our skilled educators will keep you up to date on the newest breakthroughs in Data Science by exposing you to cutting-edge approaches and industry best practices.
    • Benefit from tailored assistance, clear explanations of intricate subjects, and engaging course materials designed to enhance your learning journey and achieve optimal results in your Data Science endeavors.

    Data Science Course FAQs

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

    ACTE instructors bring 10 to 12 years of experience in their respective IT fields and work in live corporate environments, ensuring the training reflects current, practical industry knowledge. They are chosen according to the details of their expertise, teaching skills, and practical experience, so high-quality, hands-on training closely aligned with industry demands is provided.
    Yes, ACTE provides practice tests to help learners assess and enhance their knowledge and skills in Data Science. These tests are a valuable tool for tracking progress and preparing for real-world scenarios.
    • ACTE is a legend in offering placement to 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 supports 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 Interviews
    • Group Discussions
    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 Master's program in Data Science at ACTE emphasizes the core strengths of data science, focusing on areas such as data analysis, machine learning, and statistical modeling. Practical experience will be gained with Python, R, and tools for data manipulation and visualization. The program also includes hands-on applications of data cleaning, analysis, and machine learning algorithms, along with real-world projects to tackle complex data problems.
    The ACTE certification confirms your skills and knowledge in specific areas, adding value to career growth. However, recognition and significance depend on employer preferences, industry standards, and the certification's relevance to particular job roles.
    • Basic Programming Skills
    • Mathematics and Statistics
    • Data Handling and Databases
    • Understanding of Machine Learning
    • Problem-Solving and Analytical Thinking
    Yes, Many of our training programs include the opportunity to work on live projects. This will give you a sense of real-world experience, and you will be able to practice practically and build skills before ultimately becoming ready for professional roles.
    If you miss a class at ACTE, you can access recorded sessions or arrange for make-up lessons. Additionally, resources and training materials are provided to help you catch up on any missed content.
    This data science course is designed to meet your schedule and commitments. It is available in several training modes, such as classroom, one-on-one, fast-track, customized, and online.
    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, This Data Science course provides hands-on practice with real-world projects and interactive discussions. It further includes mock interviews and group sessions to improve your skills and job readiness. These activities will prepare you to tackle technical challenges and acclimate to a collaborative work environment.
    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.
    Yes, Learning Data Science is engaging, provided you have the proper resources and a systematic way of doing it. After getting down to the basics, things get intuitive, and real-world application makes the task interesting and fun.
    • Data Collection
    • Data Cleaning
    • Data Analysis
    • Data Visualization
    Yes, Data Science is an excellent option for freshers with abundant learning and growth prospects. The demand for skilled professionals is high across many industries. Appropriate training and resources can help anyone become an expert in programming, statistics, and machine learning. Also, this field is constantly evolving, and hence, it brings exciting challenges along with numerous opportunities to work on cutting-edge projects.
    Data Science has a significant impact across various industries, including healthcare, finance, and e-commerce. It drives innovation with valuable data insights for predictive analytics, hence, in understanding customer behaviour and optimizing strategies. Data Science is a great tool that creates solutions to help revolutionize these sectors by solving hard problems.
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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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