Data Analytics Training In Hyderabad-Bangalore
- Comprehensive curriculum covering essential topic
- Expert instructors providing real-world insights
- Hands-on learning through practical exercises
- Flexible options: online, in-person, or blended
- Recognized certification upon completion
Batch Details
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Data Analytics Training In Hyderabad-Bangalore
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Trainer Name
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Gopi
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Trainer Experience
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10 Years
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Next Batch Date
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21st July 24
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Course Duration
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60+ Classes
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Call us at
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+91-954 226 6111
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Why Choose Skill Centre ?
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Curriculum
Data Analytics Training In Hyderabad-Bangalore
Introduction to database and SQL
Hands on Experience in DDL , DML
Operators
Filtering Data - Where
Aggregate Functions
Sorting Functions
Primary Key and Foreign Key
Joins
Group By and Having
Window Functions
Sub Queries
Working with Strings
Python Basics
Data Types
List, Tuple, Set and Dictionaries
Conditional Statements - If, If else, elif
Control Statements - For loop and While loop
Assignmeent 1
Classes and Objects
Object Oriented Programming
Numpy
Pandas
Matplotlib
Assignmeent 2
Introduction to Power BI
Connecting to data sources and Creating basic charts and visuals
Cleansing data with Query Editor
Types of Visuals in Power BI
Managing Relations
Addition of New Columns using Power Query
Modeling and Optimization
Customisations Interactions
DAX between Tables in Power BI
Overview of Statistics
Types of data
Data Collection Methods
Range
Central Tendency
Variability
Normal Distribution
Introduction to Probability
Probability Distributions
Sampling Distributions
Introduction to Statistical Inference
Confidence Intervals
Hypothesis Testing
Introduction to EDA
Variable Analysis
Learning the Basics
Calling
Sorting and Filtering
IF
VLOOKUP
COUNTIES, SUMIFS
What is Data Analytics ?
Data analytics is the process of examining data sets to draw conclusions about the information they contain, often with the goal of discovering useful insights or supporting decision-making. It involves various techniques and tools to analyze, interpret, and present data in a meaningful way. Here’s a concise definition:
Data Collection: Gathering relevant data from diverse sources.
Data Cleaning and Preparation: Processing and refining data to ensure accuracy and completeness.
Data Analysis: Applying statistical methods and algorithms to uncover patterns and insights.
Data Visualization: Presenting data findings through visual tools like graphs and charts.
Interpretation and Communication: Extracting meaningful insights and effectively conveying them to stakeholders.
Application in Decision Making: Using insights to inform strategic and operational decisions across various industries.
Why Data Analytics ?
Insight Generation: It helps organizations uncover patterns, correlations, and trends in data that can lead to valuable insights and informed decision-making.
Optimization and Efficiency: By analyzing data, organizations can optimize processes, improve efficiencies, and reduce operational costs.
Competitive Advantage: Data analytics enables businesses to gain a competitive edge by understanding customer preferences, market trends, and industry dynamics.
Risk Management: It helps in identifying potential risks and predicting outcomes, allowing organizations to mitigate risks proactively.
Personalization: Data analytics enables personalized marketing, product recommendations, and customer experiences based on individual behaviors and preferences.
Innovation: It fosters innovation by identifying new opportunities, trends, and areas for growth based on data-driven insights.
Evidence-Based Decision Making: It promotes evidence-based decision-making by relying on data rather than intuition or assumptions.
Prerequisites to Data Analytics ?
- Basic understanding of mathematics, including algebra, statistics, and probability.
- Familiarity with computer fundamentals and proficiency in using operating systems (e.g., Windows, macOS, Linux).
- Knowledge of programming languages commonly used in data analytics, such as Python or R, or readiness to learn them during the course.
- Ability to work with spreadsheets and databases for data manipulation and analysis.
- Understanding of fundamental concepts in data management and data structures.
- Critical thinking and problem-solving skills to interpret data and derive meaningful insights.
- Good communication skills to present findings effectively to stakeholders.
- Eagerness to learn new tools, techniques, and methodologies in data analytics.
Job Opportunities
Data Analytics Training In Hyderabad-Bangalore
Roles and Responsibilities:
Collecting and interpreting data to identify trends and patterns.
Cleaning and validating data to ensure accuracy and completeness.
Creating reports and visualizations to present findings to stakeholders.
Providing actionable insights to support decision-making processes.
Skills:
Data manipulation and analysis using tools like Excel, SQL, or Python/R.
Statistical analysis and data visualization techniques.
Strong attention to detail and problem-solving skills.
Communication skills to effectively convey findings to non-technical stakeholders.
Education:
Bachelor’s degree in fields such as Mathematics, Statistics, Computer Science, Economics, or related fields.
Some roles may require a Master’s degree in Data Analytics or a related discipline for advanced positions.
- Roles and Responsibilities:
- Analyzing business processes and workflows to identify opportunities for improvement.
- Using data analysis to assess business performance and KPIs.
- Collaborating with stakeholders to gather requirements and define project scopes.
- Presenting insights and recommendations to management for strategic decision-making.
- Skills:
- Business acumen and understanding of organizational processes.
- Data interpretation and analytical skills.
- Project management skills to manage timelines and deliverables.
- Effective communication and stakeholder management skills.
- Education:
- Bachelor’s degree in Business Administration, Management, Finance, or a related field.
- Additional certifications in Business Analysis or related disciplines can be advantageous.
Roles and Responsibilities:
Developing and applying machine learning algorithms and statistical models to analyze data.
Mining and cleaning data from various sources to prepare for analysis.
Designing experiments and interpreting results to solve complex business problems.
Collaborating with cross-functional teams to integrate data-driven solutions into products or services.
Skills:
Proficiency in programming languages like Python, R, or Scala.
Advanced statistical analysis and machine learning techniques.
Knowledge of big data technologies (e.g., Hadoop, Spark) for handling large datasets.
Strong problem-solving and critical thinking abilities.
Education:
Typically requires a Master’s or Ph.D. in Computer Science, Statistics, Mathematics, or a related field.
Practical experience with data science projects and certifications in machine learning or data science are often preferred.
Roles and Responsibilities:
Gathering and analyzing business data to create reports, dashboards, and visualizations.
Monitoring key performance indicators (KPIs) and providing insights into business trends.
Collaborating with stakeholders to understand reporting requirements and deliver actionable insights.
Maintaining and optimizing BI platforms and tools for data visualization.
Skills:
Proficiency in BI tools such as Tableau, Power BI, or QlikView.
SQL querying skills to extract and manipulate data from databases.
Data visualization techniques to present findings effectively.
Understanding of business processes and KPIs in relevant industries.
Education:
Bachelor’s degree in Information Systems, Computer Science, Business Administration, or a related field.
Certifications in BI tools or courses in data visualization can enhance candidacy.
Roles and Responsibilities:
Analyzing financial data to assess performance, profitability, and financial health.
Forecasting future trends based on historical data and economic indicators.
Creating financial models and reports to support budgeting and investment decisions.
Presenting financial analysis findings to senior management or clients.
Skills:
Financial modeling techniques and proficiency in Excel or other financial software.
Analytical skills to interpret complex financial data and trends.
Knowledge of accounting principles and financial regulations.
Communication skills to explain financial analysis to non-financial stakeholders.
Education:
Bachelor’s degree in Finance, Accounting, Economics, or a related field.
Professional certifications such as Chartered Financial Analyst (CFA) or Certified Financial Analyst (CFA) can be beneficial.
Roles and Responsibilities:
Analyzing healthcare data to improve patient outcomes and operational efficiency.
Identifying trends in patient care, clinical outcomes, and healthcare costs.
Ensuring compliance with healthcare regulations and standards.
Collaborating with healthcare professionals to optimize healthcare delivery.
Skills:
Knowledge of healthcare data sources and terminology (e.g., ICD codes, EMR systems).
Analytical skills to interpret healthcare data and identify patterns.
Understanding of healthcare policies, regulations, and compliance requirements.
Communication skills to work effectively with healthcare professionals and stakeholders.
Education:
Bachelor’s degree in Health Informatics, Healthcare Management, Statistics, or a related field.
Certifications in Healthcare Informatics or experience with healthcare data analytics are advantageous.
Data Analytics Testing Certification
Course Completion:
- Finish all required coursework, assignments, and exams.
- Attend lectures, workshops, and discussions as required.
- Participate in group projects or case studies.
- Meet minimum attendance and participation requirements.
- Achieve passing grades or scores as outlined by the course syllabus.
Certificate of Completion:
- Receive an official certificate from the course provider or institution.
- Verify completion of all course requirements.
- Confirm accreditation status of the course provider.
- Ensure the certificate includes relevant details such as course title, completion date, and instructor signatures if applicable.
- Store and maintain the certificate in a safe place for future reference.
Skill Validation:
- Apply learned skills in practical scenarios or real-world projects.
- Demonstrate proficiency in data collection, cleaning, analysis, and visualization.
- Use statistical methods and tools effectively to derive insights from data.
- Present findings and recommendations clearly and effectively to stakeholders.
- Receive feedback or evaluations on project outcomes to validate skills.
Digital Badges:
- Obtain digital badges or credentials from the course provider or certification body.
- Display badges on professional networking sites such as LinkedIn.
- Provide clickable verification of certification and achievement.
- Update badges with renewal or expiration dates if applicable.
- Link badges to detailed information about the certification and its requirements.
Continued Learning:
- Stay informed about emerging trends, tools, and techniques in data analytics.
- Participate in webinars, workshops, or advanced courses to deepen knowledge.
- Pursue additional certifications or specializations related to data analytics.
- Join professional organizations or forums to network with peers and industry experts.
- Incorporate feedback from peers, mentors, or supervisors to continually improve skills and knowledge.
Preparation:
- Prerequisites: Ensure you meet the prerequisites for SailPoint IIQ development, including proficiency in Java programming, understanding of IAM principles, web technologies, databases, APIs, and cybersecurity.
- Training: Attend official SailPoint training courses specific to IdentityIQ development. These courses provide in-depth knowledge and hands-on experience with SailPoint’s development tools and methodologies.
Exam Registration:
- Register for the SailPoint certification exam through the SailPoint Certification Portal or a designated testing provider.
Study and Practice:
- Review the SailPoint IIQ documentation, study guides, and recommended resources provided by SailPoint.
- Practice using SailPoint IIQ in a development environment to gain practical experience with customization, scripting, and integration tasks.
Take the Exam:
- Schedule and take the certification exam. The exam typically consists of multiple-choice questions and may include scenario-based questions that assess your understanding of SailPoint IIQ development concepts, best practices, and use cases.
Passing the Exam:
- Achieve a passing score on the exam to earn the certification. SailPoint usually provides immediate feedback on your exam results.
Certification Renewal:
- Maintain your certification by fulfilling any continuing education requirements or renewal exams as stipulated by SailPoint. Certifications may have expiration dates that require periodic renewal to ensure continued relevance and expertise.
Course Outcome ?
Data Analytics Training In Hyderabad-Bangalore
- Understanding fundamental concepts and principles of data analytics.
- Ability to collect, clean, and preprocess data for analysis.
- Proficiency in using statistical methods and tools to analyze data.
- Skills in data visualization to effectively communicate findings.
- Knowledge of various data analysis techniques such as regression, clustering, and classification.
- Familiarity with programming languages commonly used in data analytics (e.g., Python, R).
- Ability to interpret and present analytical results to stakeholders.
- Understanding ethical considerations and best practices in data analytics.
- Practical experience through hands-on projects or case studies in real-world scenarios.
- Preparation for certifications or further specialization in specific areas of data analytics.
Mode Of Trainings
Data Analytics Training In Hyderabad-Bangalore
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Data Analytics Training In Hyderabad-Bangalore
General FAQs
Data Analytics Training In Hyderabad-Bangalore
Explanation of what data analytics entails and its relevance in various industries.
Description of the target audience and the benefits of learning data analytics skills.
Overview of the curriculum, including topics covered such as data manipulation, statistical analysis, and data visualization.
Information on any required prior knowledge or skills needed to enroll in the course, such as familiarity with programming languages or basic statistics.
Explanation of any certifications or accreditation the course institute holds, and whether completion leads to a recognized certification.
Information on how classes are conducted (lectures, labs, projects), and whether there are opportunities for hands-on experience.
Description of any networking events, job placement assistance, or connections with industry partners provided by the institute.
Details on academic support, access to instructors, tutoring, technical support, and other student services offered.
Overview of potential career paths, job roles, and industries where data analytics skills are in demand.