Data Analytics Training In Coimbatore 100% Job Guarantee

  • Classes for Beginners and Experts.
  • Best MNC Interview Questions Answered.
  • Budget-friendly fees and a curriculum created by industry experts.
  • Presented by a Certified Expert in Data Analytics Training with Over 9 Years of Experience.
  • The following batch of data analytics training will start this week. Put Your Name Into Action Right Now!
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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.

     

    Exploring COVID-19 Data

    Analyze COVID-19 data to visualize trends, identify hotspots, and explore factors influencing the spread of the virus.

     

    Churn Prediction

    Build a model to predict customer churn (i.e., when customers stop using a service) based on historical customer data.

     

    Anomaly Detection

    Identify anomalies or outliers in a dataset using statistical techniques or machine learning algorithms.

     

    Image Object Detection

    Develop an object detection model to identify and locate objects within images using convolutional neural networks (CNNs).

     

    Customer Churn Prediction with Survival Analysis

    Use survival analysis techniques to predict the time until customer churn and understand the factors influencing churn dynamics.

     

    Dynamic Pricing Optimization

    Develop a dynamic pricing strategy based on demand forecasting models and market dynamics to maximize revenue.

     

    Graph Neural Networks for Social Network Analysis

    Apply graph neural networks to analyze social networks and identify influential nodes or communities.

     

    Real-Time Anomaly Detection in IoT Data

    Implement streaming analytics techniques to detect anomalies in real-time data streams from IoT devices.

     

    Dialogue Generation with Generative Models

    Train generative models (e.g., GPT, BERT) to generate human-like dialogue responses based on conversational context.

    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
    Nisha

    Experience : 3 Years

    Nisha specializes in analyzing unstructured text data from various sources, including social media platforms, and extracting valuable insights using advanced text analytics techniques.

    Specialized in :Text Analytics, Sentiment Analysis, Social Media Mining, Web Scraping.

    Instructor
    Arjun

    Experience : 4 Years

    Arjun is experienced in applying geospatial analytics techniques to address environmental and urban planning challenges, with proficiency in GIS and remote sensing technologies.

    Specialized in : Spatial Analytics, Geographic Information Systems (GIS), Remote Sensing, Geospatial Data Visualization.

    Instructor
    Meena

    Experience : 4 Years

    Meena specializes in retail analytics and merchandising optimization, with a focus on driving sales forecasting and maximizing customer lifetime value.

    Specialized on :Retail Analytics, Sales Forecasting, Customer Lifetime Value (CLV) Analysis, Merchandising Optimization.

    Data Analytics Training Overview

    Data analytics is the process of examining, cleansing, transforming, and interpreting large volumes of data to uncover meaningful insights, patterns, and trends that can inform decision-making and drive business growth. By leveraging various techniques, tools, and methodologies, data analytics enables organizations to extract valuable insights from diverse data sources, including structured and unstructured data, to gain a competitive advantage. From descriptive analytics, which summarizes and visualizes historical data, to predictive analytics, which forecasts future outcomes, and prescriptive analytics, which recommends optimal courses of action, data analytics encompasses a wide range of activities aimed at extracting actionable insights from data. With the proliferation of data sources and the advent of technologies such as machine learning and artificial intelligence, data analytics has become increasingly sophisticated, enabling organizations to derive deeper insights, make more informed decisions, and unlock new opportunities for innovation and growth in today's data-driven world.

    Additional Information

    Data Analytics Uses

    Data analytics has a wide range of uses across various industries and domains, enabling organizations to leverage data-driven insights to achieve a variety of objectives. Some common uses of data analytics include:

    • Data analytics is used to gather, analyze, and visualize data from various sources to provide insights into business performance, customer behavior, market trends, and competitive landscape. Business intelligence tools and dashboards enable organizations to monitor key metrics, identify opportunities for improvement, and make informed decisions to drive growth and profitability.
    • Data analytics is employed to analyze customer data, such as demographics, purchase history, and browsing behavior, to segment customers, personalize marketing campaigns, and target specific customer segments with relevant offers and promotions. Marketing analytics helps organizations optimize marketing strategies, improve customer engagement, and increase return on investment (ROI).
    • In the financial sector, data analytics is used for financial modeling, risk assessment, fraud detection, and portfolio optimization. By analyzing historical and real-time data, financial institutions can identify trends, predict market movements, and mitigate risks to ensure regulatory compliance and safeguard assets.
    • Data analytics is applied to optimize operational processes, supply chain logistics, and inventory management. By analyzing data on production output, transportation routes, and inventory levels, organizations can identify bottlenecks, streamline operations, and improve efficiency to reduce costs and enhance customer satisfaction.

    Data Analytics Programmer’s Potential Career Paths

    Data analytics offers a multitude of potential career paths for programmers with varying levels of expertise and interests. Here are some potential career paths for data analytics programmers:

    • Entry-level professionals often start their careers as data analysts, responsible for collecting, cleaning, and analyzing data to derive insights and support decision-making processes. Data analysts use statistical analysis, data visualization, and SQL querying skills to interpret data and communicate findings to stakeholders.
    • Business intelligence developers design and develop data solutions and analytical tools to support business intelligence and reporting needs. They work with databases, ETL (extract, transform, load) processes, and BI tools to create dashboards, reports, and data visualizations that provide actionable insights to business users.
    • Data engineers are responsible for designing, building, and maintaining data pipelines and infrastructure to support data analytics and machine learning workflows. They work with big data technologies, such as Apache Hadoop, Apache Spark, and cloud-based data platforms, to ingest, process, and store large volumes of data efficiently.
    • Data scientists leverage advanced statistical analysis, machine learning, and programming skills to build predictive models and uncover patterns and trends in data. They work on complex data science projects, such as predictive modeling, natural language processing, and computer vision, to solve business problems and drive innovation.

    What Are the Requirements for an Data Analytics Training Course?

    The requirements for a data analytics training course can vary depending on the specific program, level of expertise, and target audience.

    • Many data analytics training courses require participants to have a minimum educational background, such as a bachelor's degree in a related field like computer science, statistics, mathematics, engineering, or a related discipline. Some courses may specify certain prerequisites or recommend foundational knowledge in areas such as mathematics, statistics, programming, or database management.
    • Participants should have basic computer literacy skills, including proficiency in using operating systems (e.g., Windows, macOS, Linux), web browsers, and office productivity software (e.g., Microsoft Excel, Google Sheets). Familiarity with file management, keyboard shortcuts, and navigating software applications is also helpful.
    • Depending on the course curriculum and focus, participants may be expected to have some level of programming proficiency. Common programming languages used in data analytics include Python, R, SQL, and others. Courses may require participants to have basic knowledge of programming concepts such as variables, loops, conditionals, functions, and data structures.
    • Data analytics involves quantitative analysis and statistical reasoning, so participants should have a basic understanding of mathematical and statistical concepts. Topics such as probability, descriptive statistics, inferential statistics, hypothesis testing, and regression analysis are commonly covered in data analytics training courses.

    Reasons to Consider Enrolling in Data Analytics Training

    • Data analytics skills are in high demand across industries, and professionals with expertise in data analytics are well-positioned to advance their careers. By acquiring data analytics skills through training, individuals can enhance their job prospects, unlock new career opportunities, and command higher salaries in fields such as data analysis, business intelligence, data science, and more.
    • In today's data-driven world, organizations rely on data analytics to make informed decisions, drive business growth, and gain a competitive edge. By gaining proficiency in data analytics tools, techniques, and methodologies, individuals can become valuable assets to their employers and contribute meaningfully to organizational success.
    • Data analytics training equips individuals with the skills and knowledge needed to analyze large volumes of data, identify trends and patterns, and derive actionable insights to address business challenges and opportunities. Whether it's optimizing operations, improving marketing strategies, or enhancing customer experiences, data analytics can help organizations make data-driven decisions to achieve their goals.
    • The field of data analytics is constantly evolving, with new technologies, tools, and techniques emerging regularly. By enrolling in data analytics training, individuals can stay updated with the latest trends and developments in the field and continuously expand their skill set to remain relevant and competitive in the job market.
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    Data Analytics Training Objectives

  • Basic understanding of statistics, mathematics, and/or programming languages.
  • Familiarity with data analysis concepts and techniques.
  • Prior experience or education in data analysis or related fields.
  • Completion of specific training courses or programs.
  • Proficiency in using data analytics tools and software.
  • Validates your skills and expertise in Data Analytics, providing recognition and credibility to potential employers.
  • Enhances your marketability and competitiveness in the job market, increasing your chances of securing employment and career advancement opportunities.
  • Provides access to a network of certified professionals and resources for ongoing support and collaboration.
  • Helps you stay current with industry standards and best practices, ensuring that your skills remain relevant and up-to-date in a rapidly evolving field.
  • Data Analyst
  • Business Analyst
  • Data Scientist
  • Business Intelligence Developer
  • Data Engineer
  • Machine Learning Engineer
  • Data Architect
  • Financial Analyst
  • Marketing Analyst
  • Healthcare Analyst
  • Increasing your visibility and credibility to potential employers, leading to more job opportunities and higher-paying roles.
  • Providing recognition for your skills and expertise, enhancing your professional reputation and marketability within the industry.
  • Demonstrating your commitment to continuous learning and professional development, which is highly valued by employers and hiring managers.
  • Equipping you with the knowledge and skills needed to tackle complex data analytics challenges and take on leadership roles within organizations.
  • Offering opportunities for networking and collaboration with other certified professionals, which can lead to mentorship, career guidance, and new career opportunities.
  • 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
  • Entry-level certifications for beginners or individuals new to the field of Data Analytics.
  • Intermediate certifications for professionals with some experience in Data Analytics looking to advance their skills and career.
  • Advanced certifications for experienced Data Analytics professionals seeking to specialize in specific areas or demonstrate mastery of advanced techniques and concepts.
  • Vendor-specific certifications offered by technology companies or software vendors for their respective Data Analytics tools and platforms.
  • Industry-recognized certifications accredited by professional organizations or associations, providing validation of skills and expertise in Data Analytics.
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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 your 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.

    • Real-world experience is not always a strict requirement for obtaining a Data Analytics certification. Many certification programs are designed to accommodate individuals with varying levels of experience and background in Data Analytics. While practical experience can be beneficial for understanding real-world applications and scenarios, certification programs typically focus on testing candidates' knowledge and proficiency in Data Analytics concepts, tools, and techniques.

    While obtaining 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.

    Specific requirements prior to taking Data Analytics certification exams may vary depending on the certification program. However, common prerequisites may include a basic understanding of statistics, mathematics, and/or programming languages, as well as prior experience or education in data analysis or related fields. Some certification programs may also require candidates to complete specific training courses or programs before taking the certification exam.

  • Reviewing the exam objectives and syllabus to understand the topics covered.
  • Enrolling in a formal training course or program to gain knowledge and skills in Data Analytics concepts, tools, and techniques.
  • Practicing with sample questions and mock exams to familiarize yourself with the exam format and assess your readiness.
  • Studying relevant textbooks, online resources, and study guides to reinforce your understanding of key concepts and topics.
  • Taking regular breaks, staying organized, and managing your time effectively to ensure thorough preparation for the exam.

    • Yes, many Data Analytics certification exams offer online testing options, 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 practical experience can be beneficial for obtaining 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 the investment of time and money in ACTE Data Analytics Certification is justified 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

    Rogan Fresher

    Financial Analyst | Hyderabad

     Got 7 LPA Package

    More Details : The comprehensive course curriculum covered financial modeling, data analysis, and investment strategies. With the practical skills gained from ACTE, I secured a Financial Analyst position in a leading financial institution.

    Rahul Fresher

    AI Trainer | Chennai

     Got 10 LPA Package

    More Details :Enrolling in ACTE's Data Analytics course was a game-changer for my career aspirations as a fresher looking to become a Marketing Analyst. Thanks to ACTE, I successfully transitioned to a Marketing Analyst role in a renowned marketing firm.

    Priyanka Fresher

    Healthcare Data Analyst | Bengalure

     Got 8 LPA Package

    More Details : Transitioning from a fresher to a Healthcare Data Analyst seemed daunting until I joined ACTE Training Institute. Today, I am proud to say that I am working as a Healthcare Data Analyst, thanks to the guidance and support of ACTE.

    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

  • Data Analyst
  • Data Scientist
  • Business Analyst
  • Data Engineer
  • Database Administrator
  • Business Intelligence Analyst
  • Data Visualization Specialist

    • Feature selection is crucial in Data Analytics because it helps improve model performance, interpretability, and efficiency. By selecting the most relevant features or variables for analysis, unnecessary noise and redundancy in the data can be reduced, leading to more accurate and interpretable models. Feature selection also helps mitigate the risk of overfitting, where a model learns patterns specific to the training data but fails to generalize to new data. Overall, feature selection plays a critical role in optimizing model performance and enhancing the interpretability of Data Analytics models.

  • Data quality issues
  • Scalability and performance
  • Model complexity
  • Overfitting and underfitting
  • Data privacy and security
  • Interpretability and explainability
  • Splitting data
  • Cross-validation
  • Performance metrics
  • Hyperparameter tuning
  • Bias-variance trade-off
  • Model comparison
  • Version control
  • Documentation
  • Modularization
  • Automation
  • Environment management
  • Testing
  • Continuous integration and deployment (CI/CD)
  • Data quality issues
  • Data privacy and security concerns
  • Interpretability and explainability
  • Scalability and performance
  • Model complexity and overfitting
  • Bias and fairness
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  • Set clear goals
  • Create a study schedule:
  • Stay organized
  • Actively participate
  • Seek help when needed
  • Practice regularly
  • 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. Many 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. 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.

    Yes, many online Data Analytics courses do provide participants with certificates upon course completion. These certificates serve as proof of your participation and completion of the training program, which can be valuable for showcasing your skills and qualifications to potential employers or advancing your career in Data Analytics. However, it's essential to verify the accreditation and recognition of the certificate issuer to ensure its credibility and relevance in the industry.

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    Corporate Data Analytics training refers to training programs and initiatives designed specifically for employees within a corporate or organizational setting. These programs aim to equip employees with the knowledge, skills, and tools necessary to effectively analyze data, derive insights, and make data-driven decisions to support business objectives and strategies.

  • Improved decision-making
  • Enhanced efficiency and productivity
  • Competitive advantage
  • Innovation and agility
  • Talent retention and development
  • Compliance and risk management
  • Data-driven decision-making
  • Competitive advantage
  • Innovation and agility
  • Operational efficiency
  • Risk management and compliance
  • Talent development
  • Introductory Data Analytics courses
  • Advanced Data Analytics courses
  • Industry-specific Data Analytics training
  • Customized Data Analytics training
  • Instructor-led training (ILT)
  • Online or e-learning courses
  • Blended learning programs
  • Hands-on workshops and labs
  • On-the-job training
  • Working with real-world datasets
  • Using Data Analytics tools and software
  • Completing projects and case studies
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