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Data Science Course Online

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  • Data Science Through Interactive Learning
  • Data Science courses, both basic and advanced
  • Lifetime access to the student portal and study materials
  • Over 12,402 students have been trained, with 350+ prospects recruited
  • Taught by a Certified Data Science Expert with over 9 years of experience
  • Upcoming Data Science Training Batch Starts This Week –  Enroll Your Name Now!

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INR 18000

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INR 22000

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Upcoming Batches

22-Apr-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

17-Apr-2024
Mon-Fri

Weekdays Regular

08:00 AM & 10:00 AM Batches

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

20-Apr-2024
Sat,Sun

Weekend Regular

(10:00 AM - 01:30 PM)

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

20-Apr-2024
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Weekend Fasttrack

(09:00 AM - 02:00 PM)

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

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Enhance Your Profession with Our Data Science Training Program

  • Our Data Science training curriculum covers all areas of the discipline, from fundamental principles to advanced methodologies, and provides students with the necessary skills to succeed in this competitive sector.
  • Our students acquire practical experience through hands-on exercises, real-world projects, and case studies, which prepare them to face the difficulties of data science in real-world circumstances.
  • Our specialized placement assistance staff provides interview preparation workshops to ensure that students are well-equipped to demonstrate their talents and pursue lucrative data science job prospects.
  • We teach a wide range of data science topics, such as machine learning, data visualization, and predictive analytics, allowing students to focus on their areas of interest.
  • Our Data Science training program, created in conjunction with industry experts, is continually updated to reflect the most recent trends and breakthroughs, ensuring that students learn current and in-demand skills.
  • Explore cutting-edge techniques like deep learning, natural language processing, and big data analytics to stay ahead of the curve in data science.
  • Graduates of the program can follow a variety of professional routes, including Data Scientist, Machine Learning Engineer, Business Analyst, and others, resulting in countless intriguing prospects in the sector.
  • Classroom Batch Training
  • One To One Training
  • Online Training
  • Customized Training
  • Enroll Now

This is How ACTE Students Prepare for Better Jobs

PLACED IMAGE ACTE

Course Objectives

Earning the Data Science Certification signifies expertise in a broad range of data science principles, techniques, and tools crucial for extracting valuable insights and conducting comprehensive data analysis.

  • Enhanced Career Opportunities
  • Practical Experience
  • Expert Guidance
  • Industry-Relevant Curriculum
  • Networking Opportunities
  • Career Advancement
  • Machine learning,
  • Data visualization,
  • Programming languages (such as Python or R),
  • Data manipulation,
  • Data mining,
  • Big data technologies,
  • Statistics.
  • Master fundamental concepts.
  • Gain practical experience.
  • Understand machine learning.
  • Develop data-driven solutions.
  • Prepare for certifications.

Yes, a Data Science education is beneficial for future prospects owing to its high demand in numerous sectors, which provides lucrative work chances, competitive advantages, and doors for professional progression and growth.

  • Data Analyst
  • Machine Learning Engineer
  • Data Scientist
  • Business Intelligence Analyst
  • Data Engineer
  • AI Engineer
  • Data Consultant
  • Research Scientist

Data Science courses often need a good foundation in mathematics and statistics, expertise in programming languages such as Python or R, experience with databases and data processing tools, and critical thinking abilities.

List the advantages of completing a Data Science course?

Completing a Data Science course provides benefits such as in-demand skills, hands-on experience, and invaluable networking opportunities. Furthermore, it offers career assistance for effective placement in the data science industry.

What are the course aims for Data Science?

The Data Science course aims to teach participants fundamental and advanced data science ideas, give hands-on experience with data analysis tools and methodologies, encourage critical thinking and problem-solving abilities, and prepare them for real-world data issues.

How will my career grow after finishing the Data Science course?

After finishing the Data Science course, individuals can improve their careers by taking on greater responsibilities, focusing in certain fields of data science, seeking higher certifications or degrees, and contributing to creative data-driven projects.

Is the Data Science course noteworthy?

Yes, the Data Science course is notable and significant in the area because of its relevance in today's data-driven world, the strong need for experienced data professionals, and the critical role data science plays in driving corporate success and innovation.

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Overview of Data science

Data science is a rapidly evolving multidisciplinary subject that uses techniques from statistics, computer science, mathematics, and domain-specific expertise to extract meaningful insights and information from data. Data science is fundamentally concerned with the collection, preparation, and analysis of data in order to discover patterns, trends, and correlations. Exploratory data analysis (EDA) allows practitioners to obtain a better knowledge of data features and distributions, frequently utilizing statistical approaches to draw conclusions and test hypotheses. Machine learning methods allow computers to learn from data and make predictions or judgments without explicit programming, whereas predictive modeling approaches use mathematical models to foresee future outcomes. Data visualization is essential for effectively expressing insights, since it uses charts, graphs, and dashboards to graphically explain complicated information. With the growth of big data, data scientists rely on modern technologies like Hadoop and Spark to rapidly handle and analyze massive amounts of data. Deep learning, a type of machine learning, has transformed jobs such as image recognition and natural language processing by teaching neural networks with numerous layers to recognize complicated patterns. However, ethical and legal issues are critical to responsible data science activity, as they ensure data privacy, security, fairness, and openness. Overall, data science is evolving quickly, driving innovation and decision-making across industries and influencing the future of business and society.

 

 

Additional Info

Future Scope of Data Science:

In order to begin or advance in the profession of Data Science, formal education is essential. Essential components of any Data Science curriculum include the following:

  • Increasing Demand: As businesses increasingly rely on data-driven decision-making, the demand for qualified data scientists is expected to increase considerably. This trend emphasizes the rising relevance of data science knowledge in achieving company success and innovation.
  • Automation and Augmentation: Data science tools and platforms will evolve to automate repetitive operations, supplement human decision-making, and enable self-service analytics, allowing enterprises to obtain insights more effectively.
  • Interdisciplinary cooperation: Data science will increasingly include cooperation with specialists in disciplines such as computer science, mathematics, and domain-specific expertise, boosting innovation and cross-disciplinary thinking.
  • Data Privacy and ethical: As data privacy and ethical concerns develop, appropriate data governance and transparency in data science processes will become more important.
  • Career Opportunities: As data science's breadth and applications develop, people with skills in data analysis, machine learning, and statistical modeling will have a wide range of career options.
  • Continuous Learning: In order to keep current with the newest trends, tools, and approaches in the industry, data science professionals must engage in continuous learning and upskilling activities.
  • Impact on Society: Data science will have a significant impact on society, propelling progress in fields such as healthcare, education, transportation, and sustainability.

Tools used in Data Science:

  • Python: Renowned for its versatility in data science, Python serves as a primary language due to its extensive libraries such as Pandas for data manipulation, NumPy for numerical computing, and Scikit-learn for machine learning tasks.
  • R Programming: Specially designed for statistical computing and graphics, R is favored for its robust packages like ggplot2 for data visualization and dplyr for data manipulation.
  • Jupyter Notebooks: These interactive environments facilitate data exploration and analysis by allowing users to write and execute Python or R code in a collaborative and user-friendly manner.
  • SQL Databases: Structured Query Language (SQL) databases like MySQL, PostgreSQL, or SQLite are pivotal for managing and querying structured data, offering efficiency in data retrieval and storage.
  • Apache Spark: This distributed computing framework is vital for processing vast datasets across clustered environments, enabling advanced analytics and machine learning tasks at scale.
  • Tableau: These powerful visualization tools empower users to create interactive dashboards and visualizations, aiding in data storytelling and insights discovery for stakeholders.
  • TensorFlow: These deep learning frameworks are essential for building and training complex neural network models, facilitating tasks such as image recognition, natural language processing, and more.
  • Hadoop: Apache Hadoop and its distributed file system (HDFS) offer scalable storage and processing for big data, enabling handling of massive volumes across distributed clusters. Coupled with Spark, it boosts data processing power for large-scale analytics.

Roles and Responsibilities of Data Science Professionals:

  • Programming Languages: Knowledge of programming languages such as Python or R, which are commonly used in data science for data manipulation, analysis, and modeling.
  • Statistical Analysis: Understanding statistical ideas and procedures such as hypothesis testing, regression analysis, ANOVA, probability distributions, and Bayesian inference.
  • Data Visualization: The ability to create representations and charts that successfully express insights and results from data using programs such as Matplotlib, Seaborn, Plotly, or Tableau.
  • Machine Learning: Understanding of machine learning algorithms and techniques for supervised learning (e.g., regression, classification), unsupervised learning (e.g., clustering, dimension reduction), and reinforcement learning.
  • Predictive Modeling: Proficiency in developing predictive models based on historical data, including linear regression, decision trees, random forests, and gradient boosting.
  • Deep Learning: Understanding of deep learning ideas and frameworks such as TensorFlow or PyTorch for image recognition, natural language processing, and time series analysis.
  • Feature Engineering: Understanding feature selection and engineering approaches for creating useful features from raw data, improving model performance, and reducing overfitting.
  • Model Evaluation and Validation: The ability to evaluate and validate machine learning models utilizing approaches such as cross-validation, hyperparameter tweaking, and model performance measures such as accuracy, precision, recall, and the F1-score.
  • Experimentation and A/B Testing: Experience developing and carrying out experiments and A/B testing to determine the efficacy of new features or algorithms.
  • Narrative and Communication: Effectively communicating data-driven insights and conclusions to stakeholders via visualization technologies like as Tableau or Power BI, as well as narrative strategies to make data intelligible and actionable.
  • Domain Knowledge: Understanding of domain-specific ideas and experience in areas like as finance, healthcare, marketing, and e-commerce is required to effectively apply data science methodologies and solutions to real-world challenges.
  • Ethical Considerations: Understanding the ethical implications of data science initiatives, such as privacy, bias, fairness, and openness in data collecting, analysis, and decision-making processes.

Explore Job Roles in Data Science:

  • Data Scientist: Data scientists gather, analyze, and interpret large datasets to support informed decision-making in organizations. They possess strong skills in statistics, programming, machine learning, and data visualization.
  • Data Analyst: Data analysts focus on extracting insights from data to identify trends and aid in business decision-making. They primarily work with structured data using tools such as SQL, Excel, and Tableau.
  • Machine Learning Engineer: Machine learning engineers develop and deploy machine learning models to automate processes or make predictions based on data. They are proficient in machine learning algorithms, programming languages like Python or R, and frameworks such as TensorFlow or PyTorch.
  • Big Data Engineer: Big data engineers build and maintain the infrastructure required to handle large volumes of data. They utilize tools like Hadoop, Spark, and Kafka to manage distributed computing systems and construct data pipelines.
  • Data Architect: Data architects design and optimize databases and data systems to ensure efficient storage, retrieval, and analysis of data. They collaborate with various stakeholders to create data models and architectures that meet business requirements.
  • Business Intelligence (BI) Developer: BI developers create data visualizations and reports to aid businesses in monitoring performance and making data-driven decisions. They often utilize BI tools like Power BI, Tableau, or QlikView.
  • Data Engineer: Data engineers are responsible for constructing and managing data pipelines to facilitate data processing and analysis. They work closely with data scientists and analysts to ensure data quality and accessibility.
  • Quantitative Analyst (Quant): Quants utilize mathematical and statistical models to analyze financial data and develop trading strategies for investment firms. They typically possess backgrounds in mathematics, statistics, or finance.
  • Data Product Manager: Data product managers oversee the development and launch of data-driven products and services. They collaborate with cross-functional teams to define product requirements and ensure alignment with user needs.
  • Data Scientist Manager/Director: Data science managers or directors lead teams of data professionals, providing guidance and direction for projects aligned with organizational goals. They possess both technical expertise and leadership skills to drive successful outcomes.
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Key Features

ACTE 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.

Curriculum

Syllabus of Data Science Course Online
Module 1: Data Science Basics
  • What is Data Science
  • Significance of Data Science in today’s world.
  • 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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Hands-on Real Time Data Science Projects

Project 1
Predictive Sales Forecasting

Utilize past data for future sales forecasting using ML models, aiding decision-making.

Project 2
Customer Segmentation EDA

Analyze data to categorize consumers for targeted marketing.

Enhance Your Skills with Our Data Science placement

  • Our Data Science placement training programs focus on interview preparation, addressing common scenarios and data science-related questions. We ensure that you are well-prepared to face a variety of interview problems.
  • Our Data Science placement training program offers internship opportunities for hands-on experience, supervised by industry specialists, allowing learners to work on real-world projects.
  • Our students get career counseling sessions to help them link their professional aspirations with Data Science prospects. These seminars help to find viable career choices and tactics for success in the industry.
  • We provide excellent placement chances with well-known organizations such as Google, Microsoft, Amazon, Facebook, and many more, guaranteeing that our candidates have great career prospects.
  • Our Data Science placement assistance helps with resume preparation and showcases Data Science training and projects to companies. This increases visibility and improves job opportunities in the field.
  • To improve training results, we encourage collaboration with a variety of data science companies. Collaborations provide real-world applications of skills, which improve learning experiences.
  • Data science-focused networking events and job fairs provide excellent opportunity to interact with prospective employers. These platforms allow you to demonstrate your talents and knowledge, which can lead to profitable professional possibilities.

Obtain Advanced Data Science Certification:

Our Data Science Certification is highly respected internationally, with major organizations underlining its relevance for professionals at all skill levels. This accreditation, which is widely recognized in the business, increases your prospects to partner with major organizations while also elevating your professional profile. Data science certification requires participating in immersive training sessions and successfully completing hands-on tasks to ensure a thorough understanding of Data Science principles and processes.

  • Data Science Professional Certificate
  • Machine Learning Engineer Certification
  • Big Data Certification
  • Python for Data Science Certification
  • R Programming Certification
  • Deep Learning Certification
  • Natural Language Processing (NLP) Certification

Definitely, having a Data Science certification can help you land a job in the area by demonstrating your dedication to lifelong learning and professional growth.

  • Expanded Career Opportunities
  • Industry Recognition and Validation
  • Up-to-Date Knowledge and Skills
  • Practical Experience
  • Networking Opportunities
  • Higher Earning Potential

Data Science certification may greatly improve professional progression possibilities by demonstrating competence and dedication to the industry, enticing potential employers and opening doors to higher-level roles.

  • Engage with Real-world Data Sets
  • Revisit Fundamental Principles
  • Utilize Official Study Guides
  • Complete Practice Examinations
  • Explore Supplementary Learning Materials

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

Building a Career with Our Data Science Trainers

  • Our Data Science Trainers are seasoned experts with extensive knowledge in the subject. They offer professional advice, mentoring, and practical insights to help you manage the complexity of data science and make sound career decisions.
  • Our Trainers provide complete training programs on a wide range of data science subjects, tools, and approaches. You'll develop a strong foundation in programming, statistical analysis, machine learning, and data visualization.
  • Our trainers emphasize practical learning, allowing you to apply theory to real projects and datasets. Through hands-on projects, case studies, and simulations, you'll develop practical skills and create a portfolio to impress employers.
  • Our trainers connect you with industry leaders, alumni, and resources to expand your network and explore opportunities. Events, lectures, and collaborations provide valuable learning experiences.
  • Our trainers provide comprehensive support for your data science career, including advice, resume feedback, interview prep, and job placement assistance. We're here to guide you, whether you're starting or advancing.
  • Our data science Trainers foster a supportive environment for peer interaction, insights sharing, and experiential learning. With online study groups, forums, and peer-to-peer activities, we encourage collaborative and enriching learning experiences.
  • Our Data Science trainers comes with extensive experience from top-tier IT firms such as Accenture, IBM, Google, and Amazon, integrating real-world insights into the learning experience.

Data Science Course Reviews

Our ACTE 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.

Nandhini

Student

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.

Sowmiya

Software Engineer

My personal experience is very good with with tutors and support staffs, they are very helpful throughout the the learning Data Science Online Course and other aspects. Growth of every student is there motive, thnak you ACTE

Ebenazar

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.

Illakiya

Student

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.

Tharani

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

Looking for better Discount Price?

Call now: +91 93833 99991 and know the exciting offers available for you!
  • ACTE is the Legend in offering placement to the students. Please visit our Placed Students List on our website
  • We have strong relationship with over 700+ Top MNCs like SAP, Oracle, Amazon, HCL, Wipro, Dell, Accenture, Google, CTS, TCS, IBM etc.
  • More than 3500+ students placed in last year in India & Globally
  • ACTE conducts development sessions including mock interviews, presentation skills to prepare students to face a challenging interview situation with ease.
  • 85% percent placement record
  • Our Placement Cell support you till you get placed in better MNC
  • Please Visit Your Student Portal | Here FREE Lifetime Online Student Portal help you to access the Job Openings, Study Materials, Videos, Recorded Section & Top MNC interview Questions
ACTE
    • Gives
Certificate
    • 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 ACTE.in's E-commerce payment system Login or directly walk-in to one of the ACTE branches in India
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Request for Class Room & Online Training Quotation

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