Data Analytics Training In Online 100% Job Guarantee

  • Classes for Beginners and Experts.
  • Best MNC Interview Questions Answered.
  • Programming for Data Analytics Through Interactive Instruction.
  • Study materials and the student portal are accessible for life.
  • 350+ recruiting prospects and over 12402 students trained.
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Curriculum in Data Analytics

Introduction to Data Analytics
  • Overview of data analytics and its importance
  • Introduction to data types and data sources
  • Overview of data analysis process
    • Introduction to tools and technologies used in data analytics (e.g., Excel, Python, R)
    Data Collection and Preprocessing
  • Data collection methods and techniques
  • Data cleaning and preprocessing techniques
  • Handling missing data and outliers
  • Data transformation and normalization
    • Exploratory Data Analysis (EDA)
  • Overview of exploratory data analysis
  • Data visualization techniques (e.g., histograms, scatter plots, box plots)
  • Descriptive statistics and summary metrics
  • Correlation analysis
    • Introduction to Statistical Analysis
  • Overview of statistical analysis in data analytics
  • Probability distributions and hypothesis testing
  • Inferential statistics and confidence intervals
  • Introduction to regression analysis
    • Machine Learning Fundamentals
  • Introduction to machine learning concepts
  • Supervised vs. unsupervised learning
  • Regression and classification algorithms
  • Model evaluation and validation techniques
    • Advanced Machine Learning Techniques
  • Feature engineering and selection
  • Dimensionality reduction techniques (e.g., PCA, t-SNE)
  • Introduction to ensemble methods (e.g., random forests, gradient boosting)
    • Time Series Analysis
  • Introduction to time series data
  • Time series visualization and decomposition
  • Time series forecasting techniques
  • Introduction to ARIMA and SARIMA models
    • Big Data Analytics
  • Introduction to big data concepts
  • Overview of distributed computing frameworks (e.g., Hadoop, Spark)
  • Introduction to NoSQL databases (e.g., MongoDB, Cassandra)
  • Handling big data with Python or R
    • Data Visualization and Dashboarding
  • Principles of effective data visualization
  • Tools and libraries for data visualization (e.g., Matplotlib, Seaborn, Tableau)
  • Dashboard design principles and best practices
  • Building interactive dashboards for data exploration and presentation
    • Case Studies and Applications
  • Real-world case studies and applications of data analytics
  • Industry-specific applications (e.g., finance, healthcare, marketing)
  • Ethical considerations in data analytics
  • Future trends in data analytics

    • Curriculum in Data Analytics

    Data Analytics Is Preferred by Over 35% of Developers. Data Analytics is the most widely used and in-demand programming language in the IT industry.

    • Overview of data analytics and its importance
    • Introduction to data types and data sources
    • Overview of data analysis process
    • Introduction to tools and technologies used in data analytics (e.g., Excel, Python, R)
    • Data collection methods and techniques
    • Data cleaning and preprocessing techniques
    • Handling missing data and outliers
    • Data transformation and normalization
    • Overview of exploratory data analysis
    • Data visualization techniques (e.g., histograms, scatter plots, box plots)
    • Descriptive statistics and summary metrics
    • Correlation analysis
    • Overview of statistical analysis in data analytics
    • Probability distributions and hypothesis testing
    • Inferential statistics and confidence intervals
    • Introduction to regression analysis
    • Introduction to machine learning concepts
    • Supervised vs. unsupervised learning
    • Regression and classification algorithms
    • Model evaluation and validation techniques
    • Feature engineering and selection
    • Dimensionality reduction techniques (e.g., PCA, t-SNE)
    • Introduction to ensemble methods (e.g., random forests, gradient boosting)
    • Introduction to time series data
    • Time series visualization and decomposition
    • Time series forecasting techniques
    • Introduction to ARIMA and SARIMA models
    • Introduction to big data concepts
    • Overview of distributed computing frameworks (e.g., Hadoop, Spark)
    • Introduction to NoSQL databases (e.g., MongoDB, Cassandra)
    • Handling big data with Python or R
    • Principles of effective data visualization
    • Tools and libraries for data visualization (e.g., Matplotlib, Seaborn, Tableau)
    • Dashboard design principles and best practices
    • Building interactive dashboards for data exploration and presentation
    • Real-world case studies and applications of data analytics
    • Industry-specific applications (e.g., finance, healthcare, marketing)
    • Ethical considerations in data analytics
    • Future trends in data analytics

    Data Analytics Training Projects

    Develop Your Data Analytics Skills With Useful and Interesting Projects.

     

    Sentiment Analysis of Tweets

    Analyze the sentiment of tweets using natural language processing (NLP) techniques and classify them as positive, negative, or neutral.

     

    Loan Default Prediction

    Build a binary classification model to predict whether a loan applicant will default or not based on historical data.

     

    Recommendation System

    Develop a basic recommendation system (e.g., movie recommendations) using collaborative filtering or content-based filtering.

     

    Time Series Forecasting with Exogenous Variables

    Forecast future demand or sales using time series analysis techniques and incorporate external factors (e.g., weather, holidays) into the model.

     

    Natural Language Generation (NLG)

    Generate human-like text based on structured data using NLG techniques, such as template-based or machine learning-based approaches.

     

    Fraud Detection

    Build an advanced fraud detection system using anomaly detection algorithms and ensemble learning techniques to detect fraudulent transactions.

     

    Autonomous Vehicle Perception

    Develop computer vision algorithms to enable autonomous vehicles to detect and classify objects in real-time.

     

    Adversarial Robustness in Deep Learning

    Investigate techniques to enhance the robustness of deep learning models against adversarial attacks.

     

    Human Activity Recognition with Wearable Sensors

    Build models to recognize and classify human activities using data from wearable sensors (e.g., accelerometers, gyroscopes).

    Best Industry Tools Covered

    Key Highlights

    Practical Training

    Global Certifications

    Flexible Timing

    Trainer Support

    Study Material

    Placement Support

    Mock Interviews

    Resume Building

    Our Instructor

    Get advice from specialists who have certifications and are currently working.

    Instructor
    Karthik

    Experience : 3 Years

    Karthik has deep expertise in quantitative finance and financial analytics, with a track record of implementing algorithmic trading strategies and risk management models.

    Specialized in : Financial Analytics, Risk Modeling, Portfolio Management, Algorithmic Trading

    Instructor
    Shreya

    Experience : 4 Years

    Shreya specializes in leveraging data analytics to drive marketing strategies and improve customer segmentation and targeting for businesses.

    Specialized in : Marketing Analytics, Customer Segmentation, Campaign Optimization, Market Research.

    Instructor
    Vikram

    Experience : 4 Years

    Vikram has extensive experience in supply chain analytics and operations management, with expertise in demand forecasting and inventory optimization techniques.

    Specialized on :Supply Chain Analytics, Demand Forecasting, Inventory Optimization, Logistics Management.

    Data Analytics Training Overview

    Data analytics is a multidisciplinary field that involves the process of examining, cleansing, transforming, and interpreting large volumes of data to uncover meaningful insights, patterns, and trends. By leveraging various techniques, tools, and methodologies, data analytics enables organizations to extract actionable insights from data and make informed decisions to drive business growth, optimize operations, and gain competitive advantages. Data analytics encompasses a wide range of activities, including descriptive analytics to summarize and visualize data, diagnostic analytics to identify root causes of problems, predictive analytics to forecast future outcomes, and prescriptive analytics to recommend optimal courses of action. With the proliferation of data sources and the advent of technologies such as machine learning and artificial intelligence, data analytics has become increasingly sophisticated, allowing organizations to extract valuable insights from structured and unstructured data sources, including text, images, and sensor data.

    Additional Information

    The Most Recent Data Analytics Tools

    • A high-performance, real-time analytics database designed for sub-second query response times on large datasets. It is commonly used for streaming data ingestion, OLAP (Online Analytical Processing), and interactive analytics.
    • It provides data versioning, schema enforcement, and data integrity for data lakes, enabling reliable and scalable data analytics.
    • An automated machine learning platform that enables organizations to build, deploy, and manage machine learning models at scale. It automates the end-to-end process of model building, from data preparation to model deployment, accelerating the time-to-value for data analytics projects.
    • An open-source business intelligence tool that allows users to create interactive dashboards and visualizations from their data without the need for SQL knowledge. It provides a simple and intuitive interface for exploring and analyzing data, making it accessible to non-technical users.
    • An open-source library for validating, documenting, and profiling data in data pipelines and analytics workflows. It helps data engineers and data scientists ensure data quality and consistency by defining and enforcing expectations on data.
    • It provides a Python-based API for defining and executing complex data workflows with built-in support for scheduling, dependency management, and error handling.
    • A distributed, real-time analytics database optimized for low-latency, high-throughput analytics on streaming and batch data. It is commonly used for powering interactive analytics dashboards, real-time monitoring, and anomaly detection.

    Future works for Data Analytics

    The future of data analytics holds numerous exciting possibilities, driven by advancements in technology, evolving business needs, and emerging trends in the data landscape. Here are some potential future directions and areas of focus for data analytics:

    • With the increasing volume and velocity of data generated by IoT devices, sensors, and other sources, real-time analytics will become even more critical. Future developments in stream processing frameworks, in-memory computing, and edge analytics will enable organizations to analyze and act on data in real-time, leading to faster decision-making and enhanced situational awareness.
    • Augmented analytics combines machine learning, natural language processing, and automation to enhance the data analysis process. Future developments in augmented analytics will focus on improving data preparation, insight generation, and storytelling capabilities, enabling users to derive actionable insights from data more effectively and intuitively.
    • AutoML platforms automate the process of building, training, and deploying machine learning models, making machine learning more accessible to non-experts and accelerating the development of predictive analytics solutions. Future advancements in AutoML will focus on improving model interpretability, robustness, and scalability, enabling organizations to leverage machine learning more effectively for decision-making and innovation.
    • XAI techniques aim to make AI models more transparent and interpretable, enabling users to understand how decisions are made and identify biases or errors. Future developments in XAI will focus on enhancing model interpretability, fairness, and accountability, making AI more trustworthy and ethically responsible.

    What new Data Analytics frameworks are there?

    • Ray is an open-source distributed computing framework designed for building scalable and efficient applications in Python. It provides a unified API for distributed task execution, enabling developers to parallelize and scale out data analytics workloads seamlessly across clusters of machines.
    • Prefect is an open-source workflow orchestration framework for building, scheduling, and monitoring data pipelines. It offers a Python-based API for defining complex workflows with dependencies, retries, and error handling, making it easier to manage and execute data analytics tasks in production environments.
    • Delta Lake is an open-source storage layer that brings ACID transactions, schema enforcement, and data versioning to Apache Spark and big data workloads. It provides reliability and scalability for data lakes, enabling organizations to build robust and scalable data analytics solutions on top of Spark.
    • Apache Iceberg is an open-source table format for storing large datasets in cloud object stores like Amazon S3 and Apache Hudi. It provides features such as schema evolution, time travel, and incremental updates, making it easier to manage and query large-scale data lakes for analytics workloads.

    Trends and Techniques used in Data Analytics

    Data analytics is a rapidly evolving field, and several trends and techniques are shaping its future. Here are some of the prominent trends and techniques currently used in data analytics:

    • Machine learning and AI techniques are increasingly being used in data analytics to uncover patterns, make predictions, and automate decision-making processes. Supervised learning, unsupervised learning, and reinforcement learning are among the popular machine learning techniques employed for tasks such as classification, clustering, regression, and anomaly detection.
    • Deep learning, a subset of machine learning, involves training artificial neural networks with multiple layers to perform complex tasks such as image recognition, natural language processing, and speech recognition. Deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are widely used for tasks that require processing large volumes of unstructured data.
    • Predictive analytics involves using historical data and statistical algorithms to forecast future trends, behaviors, or events. Techniques such as regression analysis, time series analysis, and machine learning algorithms are used to build predictive models for applications such as sales forecasting, demand prediction, and risk assessment.
    • Prescriptive analytics goes beyond predicting future outcomes by recommending actions to achieve desired outcomes. It combines techniques from optimization, simulation, and decision analysis to identify the best course of action based on various constraints and objectives. Prescriptive analytics is used in areas such as resource allocation, supply chain optimization, and strategic decision-making.
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    Data Analytics Training Objectives

  • Data Analyst
  • Business Analyst
  • Data Scientist
  • Business Intelligence Developer
  • Data Engineer
  • Machine Learning Engineer
  • Data Architect
  • Financial Analyst
  • Marketing Analyst
  • Healthcare Analyst

    • Yes, many certification tests for Data Analytics are available to take online. Online testing platforms allow candidates to conveniently schedule and take certification exams from their own location using a computer with internet access. However, it's important to ensure that the online testing platform is secure and accredited to maintain the integrity of the certification process.

  • Basic understanding of statistics, mathematics, and/or programming languages.
  • Prior experience or education in data analysis or related fields.
  • Completion of specific training courses or programs.
  • Passing a certification exam administered by a recognized certification body or organization.
  • Data manipulation and preprocessing
  • Statistical analysis and hypothesis testing
  • Data visualization and storytelling
  • Machine learning algorithms and techniques
  • Database querying and management
  • Data interpretation and decision-making
  • Critical thinking and problem-solving
  • Certified Analytics Professional (CAP)
  • IBM Data Science Professional Certificate
  • Microsoft Certified: Data Analyst Associate
  • Google Data Analytics Professional Certificate
  • SAS Certified Data Scientist
  • Cloudera Certified Associate Data Analyst
  • Tableau Desktop Specialist Certification
  • Validates your skills and knowledge in Data Analytics, enhancing your credibility and marketability to potential employers.
  • Demonstrates your commitment to professional development and continuous learning in the field.
  • Opens up career advancement opportunities and may lead to higher-paying roles within the Data Analytics industry.
  • Provides access to a network of certified professionals and opportunities for collaboration and knowledge-sharing.
  • Helps you stay competitive in a rapidly evolving job market by keeping your skills up-to-date with industry standards and best practices.
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    Industry Statistics

    Jobs / Month

    248

    Avg. Salary

    ₹ 12,55,200

    Job Roles

    Data Analyst

    Data Engineer

    Data Scientist

    Statistical Analyst

    Data Analytics Certification

    Certificate
    GET A SAMPLE CERTIFICATE
  • Validation of skills and expertise in Data Analytics, enhancing credibility and marketability to potential employers.
  • Access to a network of certified professionals and resources for ongoing support and collaboration.
  • Demonstrated commitment to professional development and continuous learning, which is highly valued by employers.
  • Enhanced understanding of Data Analytics concepts, tools, and techniques, leading to improved job performance and effectiveness in the workplace.

    • Yes, there are several Data Analytics certifications available from various certification bodies and organizations. These certifications may vary in terms of focus areas, skill levels, and industry recognition. Some popular Data Analytics certifications include Certified Analytics Professional (CAP), IBM Data Science Professional Certificate, Microsoft Certified: Data Analyst Associate, Google Data Analytics Professional Certificate, and SAS Certified Data Scientist, among others.

    While having a Data Analytics certification can enhance your skills and credentials, it does not guarantee employment. However, certified professionals may be more competitive in the job market and may have access to more job opportunities compared to non-certified individuals. Ultimately, securing employment depends on factors such as your skills, experience, qualifications, and the demand for Data Analytics professionals in the job market.

    Yes, you can pursue multiple Data Analytics course certifications to broaden your knowledge and skill set in the field. Obtaining certifications from reputable organizations or institutions can demonstrate your commitment to continuous learning and professional development, which is highly valued by employers. Pursuing multiple certifications can also enhance your marketability and competitiveness in the job market, opening up more career advancement opportunities.

    Various types of employment are available with a Data Analytics certification, including roles such as Data Analyst, Business Analyst, Data Scientist, Business Intelligence Developer, Data Engineer, Machine Learning Engineer, Data Architect, Financial Analyst, Marketing Analyst, and Healthcare Analyst, among others. These roles span across different industries and sectors, including technology, finance, healthcare, marketing, retail, and government, among others.

    Yes, many Data Analytics certification exams offer an online version, allowing candidates to conveniently schedule and take exams from their own location using a computer with internet access. Online testing platforms typically provide a secure and proctored environment to ensure the integrity of the certification process.

    While having experience in the real world can be beneficial for earning a Data Analytics certification, it is not always a strict requirement. Many certification programs are designed to accommodate individuals with varying levels of experience and background in Data Analytics. However, having practical experience and exposure to real-world data analysis projects can enhance your understanding of Data Analytics concepts and improve your performance on certification exams.

    Whether it's worth the time and money to earn an ACTE Data Analytics Certification depends on your career goals, learning objectives, and personal circumstances. ACTE is a reputable training institute known for providing high-quality education and certification programs in Data Analytics. Their certification may be valuable for individuals seeking to enhance their skills, validate their expertise, and advance their careers in Data Analytics. It's essential to research the program, consider your learning needs, and assess the potential benefits of certification before making a decision.

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    The Preferred Partner for 100+ Organizations' Hiring

    Learn from the certified and real time working professionals.

    • Over 100 firms that are looking for top talent for their open positions have come to rely on ACTE as their go-to partner.

    • Businesses have confidence in our ability to match them with the best individuals because of our considerable expertise and proven track record of success.

    • In this section, we'll examine the primary elements influencing this trust and examine how our constant commitment to excellence regularly results in remarkable results for our clients.

    Corporate Clients

    Data Analytics Course Duration and Fees

    Level Course Duration Fees Structure
    Basic 1 - 1.5 Months ₹7,000 - ₹9,000
    Advanced 1.5 - 2 Months ₹7,000 - ₹10,000

    Job Opportunities in Data Analytics

    Data Analytics is preferred by more than 35% of developers. The most widely used and in-demand programming language in the tech industry is Data Analytics.

    Salary In Data Analytics
    Reach Our Placement Officer

    You can Work as a

    Marketing AnalystHealthcare Data AnalystFraud AnalystOperations AnalystRisk AnalystSupply Chain AnalystHR Analyst

    Upcoming In-Demand Jobs

    Web AnalystSocial Media AnalystNetwork AnalystPricing AnalystSales Analyst

    Student Testimonials

    Roshan Fresher

    Statistical Analyst | Hyderabad

     Got 7 LPA Package

    More Details : Transitioning from a fresher to a Statistical Analyst required specialized skills and knowledge, which I gained through ACTE's Data Analytics course. The course curriculum included in-depth coverage of statistical methods, data analysis techniques, and programming languages like R and Python. The instructors were highly knowledgeable and provided valuable insights from their industry experience. With the practical skills acquired at ACTE, I was able to secure a role as a Statistical Analyst in a reputed organization.

    Rahul Fresher

    Quantitative Analyst | Chennai

     Got 10 LPA Package

    More Details :As a fresher aspiring to become a Quantitative Analyst, I needed a comprehensive training program that covered advanced analytics and quantitative techniques. ACTE's Data Analytics course exceeded my expectations with its rigorous curriculum and hands-on approach. The course not only taught me the theoretical concepts but also provided practical experience through real-world projects. With the support of dedicated instructors and career guidance from ACTE, I successfully transitioned to a Quantitative Analyst role.

    Priyanka Fresher

    Business Analyst | Bengalure

     Got 8 LPA Package

    More Details :Transitioning from a fresher to a Business Analyst required a blend of analytical skills and business acumen, which I developed through ACTE's Data Analytics course. The course curriculum was tailored to meet the demands of the industry, covering areas such as data interpretation, business intelligence, and strategic decision-making. The instructors were experienced professionals who shared valuable insights from their own careers. Thanks to ACTE, I am now thriving in my role as a Business Analyst.

    100% Placement

    7000+ Placed Student

    600+ Hiring Partners

    5.5 LPA Average Salary

    Recently Placed Students

    Akshara
    Sree Lakshmi
    John
    Vishali

    Data Analytics Training FAQ's

    Boost Your Coding Ability: Essential Data Analytics Training for Novices and Experienced

    Analyzing massive databases for patterns, trends, correlations, and insights that may be utilized to forecast and make choices with confidence is known as data analytics. It includes a range of data analysis approaches and procedures, such as machine learning, statistical analysis, data mining, and data visualization.

  • It helps organizations gain valuable insights from data to make informed decisions and drive strategic initiatives.
  • It enables businesses to identify trends, patterns, and correlations in data that can lead to new opportunities or improved efficiency.
  • Data Analytics allows organizations to optimize processes, enhance performance, and gain a competitive edge in the market.
  • It facilitates data-driven decision-making across all levels of an organization, leading to improved outcomes and better business results.
  • Data collection
  • Data cleaning
  • Data analysis
  • Data visualization
  • Data interpretation
  • Continuous improvement
  • Statistical analysis and hypothesis testing to draw meaningful insights from data.
  • Data visualization techniques using tools like Tableau, Power BI, or matplotlib to communicate findings effectively.
  • Critical thinking and problem-solving skills to approach complex data problems and derive actionable insights.
  • Domain knowledge in specific industries or domains to contextualize data analysis and interpret results accurately.
  • Programming languages: Python, R, SQL
  • Statistical analysis tools: Excel, SPSS, SAS, JMP
  • Machine learning libraries: scikit-learn, TensorFlow, Keras, PyTorch
  • Big data platforms: Apache Hadoop, Apache Spark, MongoDB, Cassandra
  • Data collection
  • Data preprocessing
  • Data analysis
  • Data visualization
  • Interpretation and insight generation
  • Deployment and implementation
  • Monitoring and optimization
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    Online Data Analytics training refers to educational programs or courses that are delivered over the internet, allowing individuals to learn Data Analytics concepts, techniques, and tools remotely from their own location. These training programs typically include interactive lectures, video tutorials, assignments, quizzes, and hands-on projects to help participants develop practical skills in Data Analytics.

  • Flexibility
  • Accessibility
  • Cost-effectiveness
  • Diverse learning resources
  • Self-paced learning
  • Convenience
  • Flexibility
  • Accessibility
  • Cost-effectiveness
  • Interactive learning
  • Self-paced learning
  • Course content
  • Instructor expertise
  • Reviews and ratings
  • Accreditation and certification
  • Learning format

    • Yes, online Data Analytics training can be effective for beginners, as many programs are designed to accommodate learners with varying levels of technical expertise and background knowledge. Beginners can start with introductory courses that cover foundational concepts and gradually progress to more advanced topics as they build their skills and confidence in Data Analytics. Online training programs often provide comprehensive learning materials, interactive exercises, and hands-on projects to help beginners develop practical skills and knowledge in Data Analytics.

  • Set clear goals
  • Establish a study routine
  • Actively participate
  • Seek help when needed
  • Stay organized
  • Practice regularly
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  • Working with real-world datasets
  • Using Data Analytics tools and software
  • Completing projects and case studies
  • Improved performance and productivity
  • Enhanced decision-making
  • Skill acquisition and proficiency
  • Business outcomes
  • Employee feedback and satisfaction
  • Access to training materials
  • Instructor support
  • Online communities and forums
  • Continuing education opportunities
  • Career development pathways

    • Yes, employees with different levels of programming experience can derive value from Data Analytics corporate training. Many corporate training programs are designed to accommodate learners with varying levels of technical expertise and background knowledge in programming. Beginners can start with introductory courses that cover foundational concepts and gradually progress to more advanced topics as they build their skills and confidence in Data Analytics. Advanced learners can benefit from specialized courses or workshops that focus on advanced techniques, tools, or applications of Data Analytics in specific domains or industries.

  • Per learner pricing
  • Group pricing
  • Customized pricin
  • Subscription or membership pricing
  • Empowering employees with essential Data Analytics skills and knowledge to drive data-driven decision-making and innovation within the organization.
  • Enhancing organizational agility and responsiveness through the timely analysis and interpretation of data to identify emerging trends, opportunities, and threats in the market.
  • Improving operational efficiency and effectiveness by optimizing processes, reducing costs, and enhancing performance through data-driven insights and recommendations.
  • Fostering a culture of continuous learning and improvement, where employees are encouraged to develop and apply Data Analytics skills to solve complex problems and drive strategic initiatives.
  • Differentiating your organization from competitors by leveraging Data Analytics capabilities to deliver superior products, services, and customer experiences that meet evolving market demands and preferences.
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