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Data Analytics Online Training

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100% Job Guarantee | Minimum CTC: ₹ 6 LPA

Learnovita’s Data Analytics certification course provides comprehensive training in fundamental concepts and advanced techniques essential for analyzing and interpreting data effectively. Through interactive modules, practical exercises, and real-world case studies, participants gain proficiency in data manipulation, visualization, and statistical analysis.

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  • 40+ Hrs Hands On Learning
  • 3+ Live Best Industry Projects
  • 25+ Practical Assignments
  • 24/7 Student Helpdesk

Online Classroom Batches Preferred

22- Apr- 2024
Monday (Monday - Friday)

Weekdays Regular

08:00 AM (IST)

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

17- Apr- 2024
Wednesday (Monday - Friday)

Weekdays Regular

08:00 AM (IST)

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

20- Apr- 2024
Saturday (Saturday - Sunday)

Weekend Regular

11:00 AM (IST)

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

20- Apr- 2024
Saturday (Saturday - Sunday)

Weekend Fasttrack

11:00 AM (IST)

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

Can't find a batch you were looking for?
₹21000 ₹16000 10% OFF Expires in

No Interest Financing start at ₹ 5000 / month

Data Analytics Course Overview

Our Data Analytics course provides a comprehensive overview of fundamental concepts and practical techniques essential for extracting insights from data. Students delve into statistical analysis, data visualization, and machine learning algorithms to uncover patterns and trends. Emphasis is placed on hands-on experience with popular tools like Python, R, and SQL, along with understanding data preprocessing and cleaning techniques. Real-world case studies and projects offer practical applications, allowing students to apply their knowledge to solve complex problems. Additionally, the course explores ethical considerations and best practices in data handling to ensure responsible data usage. Throughout the program, students gain proficiency in interpreting data to inform decision-making processes and drive business outcomes effectively.

Additional Info

Exploring Future Trends Data Analytics

  • Predictive analytics: Predictive analytics is the process of forecasting future events or trends using statistical algorithms, machine learning techniques, and historical data.Predictive analytics assists organizations in foreseeing future events and making well-informed decisions through the analysis of data trends and patterns.
  • Big Data Analytics: Big data analytics refers to the process of analyzing large and complex datasets to uncover hidden patterns, correlations, and other valuable insights. With the increasing volume, velocity, and variety of data generated by various sources, big data analytics plays a crucial role in understanding future trends.
  • Prescriptive Analytics: Prescriptive analytics goes beyond predictive analytics by not only predicting future outcomes but also recommending actions to achieve desired outcomes. By leveraging advanced algorithms and optimization techniques, prescriptive analytics helps organizations make data-driven decisions to shape future trends.
  • Real-time Analytics: Real-time analytics involves analyzing data as it is generated or received, enabling organizations to respond to events and trends as they happen. With the growing need for immediate insights in various industries such as finance, healthcare, and retail, real-time analytics is becoming increasingly important in shaping future trends.
  • Machine Learning: Without explicit programming, machines can learn from data and become more intelligent over time thanks to this subset of artificial intelligence. By identifying patterns and making predictions based on data, machine learning algorithms help organizations uncover future trends and make proactive decisions.
  • Natural Language Processing (NLP): Natural Language Processing (NLP) is a subfield of artificial intelligence that studies how computers and human language interact. By analyzing and understanding human language in text or speech form, NLP techniques can extract insights from unstructured data sources such as social media, customer reviews, and news articles to anticipate future trends.
  • Deep Learning: Deep learning is a subset of machine learning that uses neural networks with many layers to learn representations of data. Deep learning algorithms excel at identifying intricate patterns in large datasets, making them valuable for uncovering complex future trends in areas such as image recognition, speech recognition, and natural language processing.
  • Data Visualization: Data visualization involves presenting data in graphical or visual formats to communicate insights more effectively. By visually representing trends, patterns, and relationships in data, data visualization tools enable stakeholders to gain a deeper understanding of past trends and make informed predictions about future trends.
  • Edge Analytics: Instead of sending data to a centralized data center for processing, edge analytics analyzes data closer to the data source, at the network's edge. By performing analytics locally, edge analytics reduces latency and enables organizations to derive immediate insights from IoT devices, sensors, and other edge devices, shaping future trends in areas such as smart manufacturing, autonomous vehicles, and healthcare.
  • Ethical Data Analytics: Ethical data analytics focuses on ensuring that data analysis processes and practices adhere to ethical principles, privacy regulations, and societal norms. As organizations collect and analyze vast amounts of data, ethical considerations become increasingly important in shaping future trends, ensuring responsible data usage, and building trust with stakeholders.

Navigating the Evolving Landscape of Data Analytics Tools and Technologies

  • Programming Languages: Python is widely used for data analysis and manipulation due to its rich ecosystem of libraries such as Pandas, NumPy, and Scikit-learn. R is popular among statisticians for its robust statistical capabilities and extensive package repository for data analysis and visualization.
  • Data Visualization Tools: Tableau is known for its intuitive drag-and-drop interface, enabling users to create interactive and visually appealing dashboards and reports. Power BI, Microsoft's business analytics tool, allows users to visualize and share insights from their data through interactive reports and dynamic dashboards.
  • Database Management Systems (DBMS): SQL is fundamental for querying and managing relational databases, with variants like MySQL, PostgreSQL, and SQLite commonly used. NoSQL databases like MongoDB, Cassandra, and Elasticsearch handle unstructured or semi-structured data effectively.
  • Big Data Technologies: Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. Spark provides in-memory processing capabilities for big data analytics tasks, offering speed and efficiency.
  • Machine Learning and AI Frameworks: TensorFlow, developed by Google, is an open-source library used for building and training machine learning models. Scikit-learn, a versatile machine-learning library in Python, provides simple and efficient tools for data mining and analysis.
  • Data Integration and ETL Tools: Apache NiFi facilitates building data flows to acquire, process, and distribute data from various sources. Informatica, a leading ETL (Extract, Transform, Load) tool, is used for data integration, data quality, and master data management.
  • Statistical Analysis Software: SPSS offers a user-friendly interface and a broad range of statistical techniques, widely used for statistical analysis. SAS, known for its advanced analytics capabilities, is used for data management, predictive modeling, and statistical analysis.
  • Cloud Platforms: Amazon Web Services (AWS) offers a comprehensive suite of cloud-based services for data storage, processing, and analytics, including Amazon Redshift and Amazon EMR. Google Cloud Platform (GCP) provides scalable and cost-effective solutions for data analytics tasks through services like BigQuery and Dataflow.

Understanding Roles and Responsibilities in Data Analytics

  • Data Analyst vs. Data Scientist: Understand the distinction between these two roles. Data analysts primarily focus on interpreting data, generating reports, and conducting descriptive analyses. Data scientists, on the other hand, delve deeper into predictive modeling and machine learning algorithms and often have advanced programming skills.
  • Data Collection and Cleaning: This involves gathering data from various sources, such as databases, spreadsheets, or APIs, and ensuring its accuracy and completeness. Data cleaning is the process of identifying and correcting errors, inconsistencies, and missing values in the dataset.
  • Exploratory Data Analysis (EDA): EDA involves examining the dataset to summarize its main characteristics, often using statistical graphics and other data visualization techniques. This step helps analysts understand the structure of the data, identify patterns, and formulate hypotheses.
  • Statistical Analysis: Data analysts are expected to have a solid understanding of statistical methods and techniques. This includes hypothesis testing, regression analysis, clustering, and other multivariate analysis methods to derive meaningful insights from the data.
  • Data Visualization: Communicating insights effectively is a crucial responsibility of data analysts. They use tools like Tableau, Power BI, or Python libraries like Matplotlib and Seaborn to create visual representations of the data, such as charts, graphs, and dashboards.
  • Predictive Modeling: Data analysts may be involved in building predictive models to forecast future trends or outcomes based on historical data. This requires knowledge of machine learning algorithms, feature selection, model evaluation, and optimization techniques.
  • Business Intelligence (BI): Data analysts play a key role in providing actionable insights to support decision-making within organizations. They work closely with stakeholders to understand business requirements, identify key performance indicators (KPIs), and develop reports and dashboards to track and monitor business metrics.
  • Data Governance and Compliance: Ensuring data quality, security, and compliance with regulations such as GDPR or HIPAA is another responsibility of data analysts. They may establish data governance policies, implement data security measures, and conduct audits to ensure data integrity and confidentiality.
  • Domain Knowledge: Depending on the industry or domain they work in, data analysts need to possess domain-specific knowledge to contextualize their analyses. For example, a healthcare data analyst would need an understanding of medical terminology and regulations, while a financial analyst would need knowledge of banking and finance.
  • Ongoing Education and Skill Development: New tools, technologies, and methods are frequently developed in the data analytics industry, which is a field that is always changing. Data analysts are expected to stay updated with the latest trends, attend training sessions, and engage in continuous learning to enhance their skills and expertise.

Leading Companies in Data Analytics Recruitment

  • Google:With its advanced data analytics projects and AI-driven initiatives, Google is often at the forefront of attracting top talent in data analytics.
  • Facebook: Renowned for its data-driven approach to advertising and user engagement, Facebook is a major player in data analytics recruitment.
  • Amazon: Amazon's vast ecosystem generates massive amounts of data, requiring skilled professionals in data analytics to derive insights and improve operations.
  • Microsoft: As a leader in cloud computing and enterprise solutions, Microsoft offers numerous opportunities for data analysts to work on cutting-edge projects.
  • IBM: With its long-standing expertise in data management and analytics, IBM remains a key player in recruiting professionals in this field.
  • Apple: Apple's focus on user experience and product development involves extensive data analysis, making it a sought-after destination for data analytics professionals.
  • Netflix: Uber's ride-hailing platform generates vast amounts of data, driving the need for skilled analysts to optimize operations and improve user experiences.
  • Uber: Wipro provides IT consulting, outsourcing, and solutions, including staffing and implementation services for SAP BI.
  • LinkedIn: LinkedIn itself utilizes data analytics extensively for its platform's optimization and offers opportunities for professionals to work on similar projects.
  • Palantir Technologies: Known for its advanced data analytics and intelligence platforms, Palantir is a prominent recruiter of data analytics talent, particularly in the field of cybersecurity and defense.
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Top Skills You Will Gain
  • Exploratory Data Analysis
  • Data Visualization
  • Statistical Modeling
  • Data Mining
  • Business Intelligence
  • Data Warehousing
  • Machine Learning
  • Data Cleansing & Preparation

Data Analytics Course Key Features 100% Money Back Guarantee

  • 5 Weeks Training

    For Become a Expert
  • Certificate of Training

    From Industry Data Analytics Experts
  • Beginner Friendly

    No Prior Knowledge Required
  • Build 3+ Projects

    For Hands-on Practices
  • Lifetime Access

    To Self-placed Learning
  • Placement Assistance

    To Build Your Career

Top Companies Placement

Our Data Analytics placement initiative enhances the hiring process by connecting companies with proficient professionals skilled in deploying and enhancing Data Analytics solutions. Graduates can anticipate attractive compensation packages, alongside avenues for significant salary growth facilitated by our placement support.
  • Designation
  • Annual Salary
    Hiring Companies
  • 4L
  • 6L
  • 10L
  • 5L
  • 10L
  • 15L
  • 6L
  • 10L
  • 15L
  • 4L
  • 6L
  • 10L

Training Options

One to One Training

₹23000₹ 18000

  • Exclusive one-on-one sessions for personalized skill development.
  • Latest version curriculum is provided by industrial professionals.
  • Get industry-recognized certification with placement assistance.
  • Lifetime Access to Study Materials, Videos & Top MNC Interview Question.
  • Work on multiple Industry-specific projects to enhance your portfolio.

Online Training

₹21000₹ 16000

  • preferred
  • Schedule your sessions at your preferred timings for maximum convenience.
  • Lifetime access to high-quality self-learning and live online class recordings.
  • Attend a Free Demo before signing up.

Next Demo Sessions

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Corporate Training

Customized to your team's needs

  • Self-Paced/Live Online/Classroom modes of training available.
  • Design your own course content based on your project requirements.
  • Comprehensive certification guidance for your career advancement.
  • Get 100% Job-oriented Training With Dedicated Placement Support.
  • Receive 24x7 assistance and support to maximize your learning experience.

Self Paced Training

  • 50+ Hours High-quality Video
  • 28+ Downloadable Resource
  • Lifetime Access and 24x7 Support
  • Access on Your Computer or Mobile
  • Get Certificate on Course Completion
  • 3+ Projects
12500 ₹4500

Data Analytics Course Curriculum

Trainers Profile

Our certified trainers are seasoned professionals boasting substantial industry expertise, and actively engaged with top multinational firms. At Learnovita, our trainers are dedicated to imparting advanced insights and best practices in Data Analytics, fostering learners' preparedness to excel in today's fiercely competitive job market.

Syllabus of Data Analytics Online Course Download syllabus

  • Understanding Data Analytics
  • Importance and Applications
  • Types of Data Analytics: Descriptive, Predictive, Prescriptive
  • Tools and Technologies in Data Analytics
  • Data Collection Methods
  • Data Sources: Structured and Unstructured Data
  • Data Cleaning Techniques
  • Handling Missing Values
  • Outlier Detection and Treatment
  • Exploring Data Distributions
  • Summary Statistics
  • Data Visualization Techniques
  • Correlation Analysis
  • Dimensionality Reduction Techniques
  • Introduction to Machine Learning
  • Supervised Learning
  • Unsupervised Learning
  • Model Evaluation and Validation
  • Feature Engineering
  • Understanding Predictive Modeling
  • Regression Models
  • Classification Models
  • Ensemble Methods
  • Model Evaluation Metrics
  • Introduction to Big Data
  • Big Data Technologies: Hadoop, Spark, etc.
  • Distributed Computing
  • Scalable Data Analysis Techniques
  • Real-time Analytics
  • Natural Language Processing (NLP)
  • Deep Learning Fundamentals
  • Reinforcement Learning
  • Genetic Algorithms
  • Time Series Forecasting
  • Data Collection and Preparation
  • Analysis and Modeling
  • Interpretation of Results
  • Designing a Data Analytics Project
  • Presentation and Communication of Findings
  • Introduction to Time Series Data
  • Time Series Visualization
  • Time Series Forecasting Methods
  • Seasonality and Trend Analysis
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Industry Projects

Project 1
Customer Segmentation for Retail

Utilize data analytics to identify distinct customer groups for targeted marketing strategies.

Project 2
Predictive Maintenance in Manufacturing

Implement data-driven models to anticipate equipment failures and optimize maintenance schedules.

Project 3
Healthcare Resource Optimization

Analyze patient data to streamline resource allocation, improving efficiency and patient care outcomes.

Mock Interviews

  • Mock interviews by Learnovita give you the platform to prepare, practice and experience the real-life job interview. Familiarizing yourself with the interview environment beforehand in a relaxed and stress-free environment gives you an edge over your peers.
  • Our mock interviews will be conducted by industry experts with an average experience of 7+ years. So you’re sure to improve your chances of getting hired!

How Learnovita Mock Interview Works?

Data Analytics Training Objectives

  • Career Advancement
  • Higher Earning Potential
  • Improved Job Security
  • Versatility
  • Innovation and Efficiency
  • Cross-Functional Collaboration
  • Job Security
  • Skill Enhancement
  • Problem-Solving Abilities
  • Recognition and Reputation
Data Analytics skills are applied in diverse fields, such as marketing, finance, healthcare, and more. Professionals can extract valuable insights from data to optimize marketing campaigns, forecast financial trends, improve patient care, and enhance operational efficiency.
Data Analytics training typically covers topics like data wrangling, exploratory data analysis, statistical modeling, machine learning, data visualization, and real-world case studies. These components provide a comprehensive understanding of the data analysis process.
  • Visualization Skills
  • Algorithmic Thinking
  • Decision-Making
  • Critical Thinking
  • Collaboration
Data Analytics training enables professionals to make data-driven decisions by interpreting patterns and trends within datasets. They can derive actionable insights, identify opportunities, mitigate risks, and optimize strategies to achieve organizational goals effectively.
Data Analytics courses emphasize communication and collaboration skills, enabling professionals to convey insights derived from data to stakeholders effectively. This fosters interdisciplinary teamwork, encouraging alignment of business objectives and data-driven decision-making.
  • Practical Skill Development
  • Deep Understanding
  • Portfolio Building
  • Adaptability
  • Confidence Building
With the exponential growth of data in today's digital age, there's a high demand for skilled data professionals. Data Analytics training equips individuals with the requisite skills to meet industry demands, bridging the gap between data availability and actionable insights.
Certification validates proficiency in Data Analytics skills, providing credibility to professionals seeking career advancement or transition. It serves as tangible evidence of expertise, enhancing employability and opening doors to diverse job opportunities in the data-driven market landscape.
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Exam & Certification

Typically, prerequisites for a Data Analytics certification include a basic understanding of statistics, proficiency in programming languages like Python or R, and familiarity with data manipulation and visualization tools such as SQL and Tableau. Some certifications may require prior experience in data analysis or related fields.
Earning a Data Analytics certification can significantly boost career prospects. It demonstrates proficiency in data analysis techniques and tools, making professionals more attractive to employers seeking skilled data analysts. Certification also validates one's expertise, potentially leading to career advancement opportunities and higher salaries.
The duration of Data Analytics certification programs varies depending on factors such as the program's intensity and the individual's prior knowledge and experience. Generally, these programs can range from a few weeks to several months. Some accelerated programs may be completed in as little as a few weeks, while more comprehensive programs may span several months.
  • IBM (IBM Data Analyst Professional Certificate)
  • Microsoft (Microsoft Certified: Data Analyst Associate)
  • Google (Google Data Analytics Professional Certificate)
  • SAS (SAS Certified Data Scientist)
Professionals in data analytics should be aware of certifications such as the Certified Analytics Professional (CAP), which validates expertise in advanced analytics and data science methodologies. Additionally, the Microsoft Certified: Data Analyst Associate certification demonstrates proficiency in data analysis using Microsoft Power BI and Excel, essential tools in the field.

Recently placed students

Data Analytics Course FAQ's

Certainly, you are welcome to join the demo session. However, due to our commitment to maintaining high-quality standards, we limit the number of participants in live sessions. Therefore, participation in a live class without enrollment is not feasible. If you're unable to attend, you can review our pre-recorded session featuring the same trainer. This will provide you with a comprehensive understanding of our class structure, instructor quality, and level of interaction.
All of our instructors are employed professionals in the industry who work for prestigious companies and have a minimum of 9 to 12 years of significant IT field experience. A great learning experience is provided by all of these knowledgeable people at LearnoVita.
  • LearnoVita is dedicated to assisting job seekers in seeking, connecting, and achieving success, while also ensuring employers are delighted with the ideal candidates.
  • Upon successful completion of a career course with LearnoVita, you may qualify for job placement assistance. We offer 100% placement assistance and maintain strong relationships with over 650 top MNCs.
  • Our Placement Cell aids students in securing interviews with major companies such as Oracle, HP, Wipro, Accenture, Google, IBM, Tech Mahindra, Amazon, CTS, TCS, HCL, Infosys, MindTree, and MPhasis, among others.
  • LearnoVita has a legendary reputation for placing students, as evidenced by our Placed Students' List on our website. Last year alone, over 5400 students were placed in India and globally.
  • We conduct development sessions, including mock interviews and presentation skills training, to prepare students for challenging interview situations with confidence. With an 85% placement record, our Placement Cell continues to support you until you secure a position with a better MNC.
  • Please visit your student's portal for free access to job openings, study materials, videos, recorded sections, and top MNC interview questions.
LearnoVita Certification is awarded upon course completion and is recognized by all of the world's leading global corporations. The National Institute of Education (NIE) in Singapore and LearnoVita are the exclusive authorized Oracle, Microsoft, Pearson Vue, and PSI exam centers, as well as an authorized partner of AWS. Additionally, those who want to pass the National Authorized Certificate in a specialized IT domain can get assistance from LearnoVita's technical experts.
As part of the training program, LearnoVita provides you with the most recent, pertinent, and valuable real-world projects. Every program includes several projects that rigorously assess your knowledge, abilities, and real-world experience to ensure you are fully prepared for the workforce. Your abilities will be equivalent to six months of demanding industry experience once the tasks are completed.
At LearnoVita, participants can choose from instructor-led online training, self-paced training, classroom sessions, one-to-one training, fast-track programs, customized training, and online training options. Each mode is designed to provide flexibility and convenience to learners, allowing them to select the format that best suits their needs. With a range of training options available, participants can select the mode that aligns with their learning style, schedule, and career goals to excel in Data Analytics.
LearnoVita guarantees that you won't miss any topics or modules. You have three options to catch up: we'll reschedule classes to suit your schedule within the course duration, provide access to online class presentations and recordings, or allow you to attend the missed session in another live batch.
Please don't hesitate to reach out to us at contact@learnovita.com if you have any questions or need further clarification.
To enroll in the Data Analytics Course at LearnoVita, you can conveniently register through our website or visit any of our branches in India for direct assistance.
Yes, after you've enrolled, you will have lifetime access to the student portal's study materials, videos, and top MNC interview questions.
At LearnoVita, we prioritize individual attention for students, ensuring they can clarify doubts on complex topics and gain a richer understanding through interactions with instructors and peers. To facilitate this, we limit the size of each Data Analytics batch to 5 or 6 members.
The average annual salary for Data Analytics Professionals in India is 5 LPA to 7 LPA.
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