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Online And Classroom Training Courses With Certification

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Data Science Certification Course

4.6 (862 Ratings)
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If you aspire to become a data scientist, acquire data analytics skills, or dive into the world of data-driven insights, Data Science stands out as one of the premier fields. Data Science’s versatility spans from analyzing complex datasets to building machine learning models and deriving actionable business intelligence.

The demand for adept Data Scientists and analysts is skyrocketing, with leading companies, renowned brands, and innovative startups actively seeking data-savvy candidates. Now is the perfect moment for you to seize these opportunities by embarking on a journey to master Data Science Certification Course through the finest Data Science training course in Bangalore!

Ascent Software Training Institute Data Science Course in Bangalore

Quick Overview of Data Science Certification Course in Bangalore Offered by Ascent

Ascent, a renowned IT training institute situated in the vibrant city of Bangalore, has established itself as a premier destination for Data Science training. With a legacy of delivering quality education and a commitment to shaping the future of aspiring data scientists, Ascent’s Data Science training program stands as a beacon of excellence in the tech education landscape.

Expert Faculty
Ascent takes pride in its team of experienced and highly qualified instructors. These industry experts bring a wealth of knowledge and practical experience to the classroom, ensuring that students receive the most up-to-date and relevant Data Science training. With their guidance, students can gain valuable insights and industry best practices, setting them on the path to becoming proficient Data Scientists.

Comprehensive Curriculum
The Data Science Certification Course at Ascent covers a wide spectrum of Data Science-related topics, ranging from the fundamentals to advanced concepts. The curriculum is thoughtfully designed to provide a solid foundation in Data Science, including statistics, machine learning, data analysis, and data visualization. Additionally, the program delves into advanced Data Science features, including big data analytics and deep learning, equipping students with a well-rounded skill set.

Hands-on Learning
One of the hallmarks of Ascent’s Data Science training is its hands-on approach. Students are encouraged to apply their knowledge in practical exercises and real-world projects. This hands-on experience helps learners build confidence and competence in Data Science. From working on data analysis projects to building predictive models, students get ample opportunities to hone their skills.

Industry-Relevant Projects
To bridge the gap between theory and practical application, Ascent incorporates industry-relevant projects into its Data Science training program. These projects simulate real-world scenarios and challenges, enabling students to work on solutions that mirror the demands of the data science industry. This practical exposure is invaluable in preparing students for their future careers.

Updated Course Material
Data Science Certification course is a dynamic field, and Ascent understands the importance of staying current with industry trends. The institute regularly updates its course material to reflect the latest advancements in Data Science technology. Students can be confident that they are learning the most relevant and up-to-date Data Science concepts, ensuring their readiness for the ever-evolving data analytics landscape.

Personalized Learning
At Ascent, each student’s learning journey is personalized to cater to their unique needs and skill levels. The institute understands that every learner is different, and their Data Science training is tailored accordingly. Whether you are a beginner or have some prior data analysis experience, Ascent’s Data Science training program can be customized to meet your learning objectives.

Small Class Sizes
To facilitate effective learning and individualized attention, Ascent maintains small class sizes. This ensures that students have ample opportunities to interact with instructors, seek clarifications, and engage in meaningful discussions. Small class sizes also foster a collaborative learning environment, where students can learn from their peers.

Flexible Schedules
Ascent understands the diverse needs of its students, many of whom may have other commitments. To accommodate these needs, the institute offers flexible training schedules, including weekend and evening classes. This flexibility ensures that individuals with busy lifestyles can still pursue their Data Science training goals.

Certification
Upon successful completion of the Data Science training program, students receive a certification from Ascent. This certification is a testament to their proficiency in Data Science and can be a valuable asset when seeking employment opportunities. It serves as a recognized credential in the industry.

Job Assistance

Ascent goes the extra mile to support its graduates in their career endeavors. The institute provides job placement assistance, helping students connect with potential employers and guiding them through the job application process. Ascent’s strong industry connections can be a significant advantage for those entering the job market.

Ascent’s Data Science training in Bangalore is a comprehensive and meticulously designed program that equips individuals with the skills and knowledge needed to excel in the field of Data Science. With expert instructors, practical learning experiences, industry connections, and a commitment to personalized education, Ascent stands as a leading choice for those looking to embark on a successful journey in the ever-evolving world of data analytics and data science. Whether you are a novice in data analysis or seeking to enhance your Data Science skills, Ascent’s Data Science training program offers a pathway to success in the dynamic field of data science and analytics.

Complete Data Science Certification Course Curriculum

  • Definition and scope of Data Science.
  • Importance and applications of Data Science.
  • Data Science process: from data collection to insights.
  • Role of a Data Scientist in various industries.
  • Ethical considerations in Data Science.
  • Tools and technologies used in Data Science.
  • Overview of real-world Data Science case studies.
  • Introduction to the course structure and expectations.
  • Sources of data: structured, semi-structured, and unstructured.
  • Web scraping and APIs for data collection.
  • Data quality assessment and common issues.
  • Data preprocessing techniques.
  • Handling missing data and outliers.
  • Data transformation and normalization.
  • Data integration and feature engineering.
  • Hands-on exercises using popular data manipulation libraries.
  • Importance of EDA in understanding data.
  • Summary statistics and data visualization.
  • Distribution analysis and histogram plotting.
  • Correlation and scatter plots.
  • Box plots and violin plots.
  • Time series analysis.
  • Interactive data visualization tools.
  • Applying EDA to real datasets.
  • Basics of machine learning and its types.
  • Supervised vs. unsupervised learning.
  • Model representation and evaluation.
  • Training and testing datasets.
  • Cross-validation techniques.
  • Overfitting and underfitting.
  • Performance metrics: accuracy, precision, recall, F1-score, etc.
  • Implementing simple machine learning algorithms.
  • Linear regression and its assumptions.
  • Polynomial regression and regularization.
  • Logistic regression for classification.
  • Decision trees and random forests.
  • Support Vector Machines (SVM).
  • Naive Bayes classifier.
  • Model selection and hyperparameter tuning.
  • Hands-on projects involving regression and classification.
  • K-means and hierarchical clustering.
  • Clustering evaluation metrics.
  • Principal Component Analysis (PCA).
  • t-SNE (t-distributed Stochastic Neighbor Embedding).
  • Manifold learning techniques.
  • Applications of clustering and dimensionality reduction.
  • Visualizing high-dimensional data.
  • Case studies using clustering and dimensionality reduction.
  • Introduction to NLP and its challenges.
  • Text preprocessing: tokenization, stemming, lemmatization.
  • Bag-of-words and TF-IDF representations.
  • Sentiment analysis and text classification.
  • Named Entity Recognition (NER).
  • Word embeddings: Word2Vec, GloVe.
  • Seq2Seq models and machine translation.
  • Hands-on NLP projects using libraries like NLTK and spaCy.
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  • Basics of artificial neural networks.
  • Activation functions and backpropagation.
  • Building deep neural networks.
  • Convolutional Neural Networks (CNN) for image data.
  • Recurrent Neural Networks (RNN) for sequential data.
  • Transfer learning and pre-trained models.
  • Regularization techniques in deep learning.
  • Implementing deep learning projects using TensorFlow or PyTorch.
  • Introduction to Big Data concepts.
  • Hadoop and MapReduce framework.
  • Apache Spark for large-scale data processing.
  • Working with distributed file systems.
  • Data streaming and real-time processing.
  • Handling big data challenges: scalability, reliability.
  • Cloud computing and data science.
  • Practical exercises with big data tools.
  • Ethical considerations in data collection and usage.
  • Bias and fairness in machine learning.
  • Privacy issues and data anonymization.
  • GDPR and other data protection regulations.
  • Responsible AI and algorithmic transparency.
  • Case studies of ethical dilemmas in Data Science.
  • Implementing ethical practices in Data Science projects.
  • Class discussions on ethical challenges.
  • Forming teams and selecting project topics.
  • Project proposal and scope definition.
  • Data acquisition and preprocessing for the project.
  • Exploratory data analysis for insights.
  • Model selection and development.
  • Implementation of machine learning or deep learning techniques.
  • Evaluation metrics and performance analysis.
  • Project documentation and presentation.
  • Emerging trends in Data Science and AI.
  • Reinforcement learning and its applications.
  • Generative Adversarial Networks (GANs).
  • Explainable AI and model interpretability.
  • Quantum computing and its impact on Data Science.
  • AI ethics and regulation advancements.
  • Industry-specific applications and case studies.
  • Preparing for a career in Data Science: job roles, skills, and certifications.

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Unlock the Data Science Certification Course Program

Industry-Recognized Certification: Completing this program provides you with a prestigious certification recognized by the data science industry, which can enhance your career prospects and demonstrate your expertise to potential employers.

Certification Credibility: An Ascent Data Science Certification carries weight in the job market, signaling your expertise and commitment to excellence in the field of data science.

Global Recognition: Ascent’s data science certification is recognized globally, enhancing your opportunities for international job placements and collaborations in the data science domain.

Competitive Edge: Gain a competitive edge with specialized training in data science, allowing you to stand out among your peers in the ever-evolving field of data analysis and machine learning.

High Demand in the Job Market: Data science is a rapidly growing field, and data scientists are in high demand across industries. Earning this certification can significantly enhance your job prospects in the data science field.

Career Advancement: Certification often leads to career advancement. You’ll be better positioned for promotions and salary increases due to your specialized knowledge and skills in data science.

Higher Earning Potential: Certified data science professionals often command higher salaries than their non-certified counterparts. Your investment in the certification can pay off in the form of a more lucrative career in the data science industry.

Course Completed Certification From Ascent

Career Services By Ascent

Career Services By Ascent​
Job Assistance
Interview Preparation
Resume Building

Placement Support: Get exclusive access to our job portal with 700+ hiring partners, including top startups and product companies. Receive personalized guidance for tailored career opportunities.

Extensive Hiring Network: Connect with 700+ hiring partners, including startups and product companies that actively seek our learners for job openings.

Guided Job Search: Receive personalized mentorship for finding roles aligned with your career goals. Access relevant job listings designed for your career growth. We're committed to your advancement.

Mock Interview Preparation: 

Students will participate in multiple simulated interviews conducted by experienced technical professionals. These experts will provide valuable tips and constructive feedback to help students enhance their skills.

Individual Career Mentoring Sessions:

Engage in personalized, one-on-one sessions with career mentors to cultivate the essential skills and mindset needed to attain a coveted job aligned with a learner's educational background, prior experience, and future career goals.

Career-Focused Sessions

Access over 20 live interactive sessions with industry experts designed to impart knowledge and hands-on experience in developing skills sought after by hiring managers. These guided sessions will assist you in maintaining a structured approach to your upskilling journey.

Creating Professional Resumes and LinkedIn Profiles

Receive support from our career services team in crafting an exceptional resume and optimizing your LinkedIn profile. Learn effective strategies to capture the attention of hiring managers during the initial profile screening phase.

Who Can Apply for the Course?

  • Individuals with a bachelor’s degree and a strong desire to transition into the field of Data Science.
  • IT professionals looking to pivot their careers towards Data Science.
  • Professionals striving to enhance their IT careers by acquiring expertise in Data Science.
  • Data Scientists and Data Science project managers.
  • Freshers eager to kickstart their careers in Data Science and analytics.
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Why choose Ascent Software for Data Science Course In Bangalore?

Ascent Training By experts Virtual Class rooms

Training by Data Science Experts

We provide comprehensive Data Science training in Bangalore, guided by seasoned mentors with extensive expertise in the field.

Practical Orientations by Ascent

100% Practical-Oriented

Get hands-on experience in Data Science through our entirely practical and skill-focused course.

Corporate Classroom Trainings

Intensive Classroom Training

In Bangalore, our fully digital classrooms provide an immersive, distraction-free learning environment for intensive education.

Students will work on Realtime Projects

Work on Real Projects

During the course, you will get to work on plenty of Java-based programming and development projects for the live experience.

Job Assistance on Ascent software Training Institute

Job Assistance

Our Data Science course offers invaluable job assistance, equipping graduates with resume building, interview prep, and industry connections for successful career placement.

Professional Certification by Ascent

Professional Certification

Professional course certification enhances skills, boosts employability, validates expertise, and opens doors to better career opportunities and higher earning potential.

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Frequently Asked Questions About the Data Science Course

Data science is a multidisciplinary field that uses scientific methods, algorithms, processes, and systems to extract knowledge and insights from structured and unstructured data. It's important because it helps organizations make data-driven decisions, solve complex problems, and gain a competitive advantage.

Data science courses are suitable for anyone interested in working with data to solve problems or make informed decisions. This includes professionals in fields like business, finance, healthcare, engineering, and computer science.

Prerequisites can vary, but a strong foundation in mathematics, statistics, and programming (usually Python or R) is typically recommended. Some courses may also require prior knowledge of data analysis tools and techniques.

A data science course typically covers topics such as data preprocessing, statistical analysis, machine learning, data visualization, and big data technologies. It may also include practical exercises and projects.

Commonly used tools include Python or R for programming, libraries like pandas and scikit-learn, Jupyter notebooks for coding, and data visualization tools like Matplotlib and Seaborn.

The duration of a data science course can vary widely. Some online courses can be completed in a few weeks, while more comprehensive programs or degrees may take several months to a few years.

Data science course graduates can pursue careers as data scientists, machine learning engineers, data analysts, business analysts, and more. The demand for data science professionals is high across various industries.

Many data science courses offer certifications upon completion. These certifications can be valuable for job seekers as they demonstrate proficiency in data science skills to potential employers.

Yes, many data science courses are available online. Online courses can be just as effective as in-person courses, as they often provide video lectures, interactive assignments, and opportunities to collaborate with peers and instructors.

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The cost of a data science course can vary significantly depending on factors such as the institution, course duration, and whether it's online or in-person. It can range from free online courses to several thousand dollars for advanced programs or degrees. Scholarships and financial aid may also be available.

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