4.7Course Overview
The Advanced Data Analysis Techniques certification typically represents a formal recognition of one’s expertise in leveraging complex data analysis methods. It encompasses a range of skills, from sophisticated statistical models and machine learning algorithms to big data analytics tools. This certification is valued across industries that rely on data to drive decision-making, optimize operations, or innovate products and services. By mastering these techniques, certified professionals can extract meaningful insights, identify trends, and make predictions from vast datasets—providing a competitive edge to businesses that operate in data-driven sectors such as finance, healthcare, marketing, and technology.
This is a Rare Course and it can take up to 3 weeks to arrange the training.
Top Companies Hiring Advanced Data Analysis Techniques Certified Professionals
Top companies hiring Advanced Data Analysis Techniques certified professionals include Google, Microsoft, Amazon, IBM, Facebook, Deloitte, KPMG, EY, McKinsey & Company, and Accenture. These companies value advanced analytics skills for strategic decision-making, market insights, and enhancing business operations.The learning objectives of an Advanced Data Analysis Techniques course typically involve the following:
1. Develop a comprehensive understanding of various advanced data analysis methodologies.
2. Gain proficiency in applying statistical models and machine learning algorithms to real-world datasets.
3. Enhance skills in data preprocessing, feature selection, and dimensionality reduction techniques.
4. Understand the principles of predictive modeling and be able to evaluate model performance.
5. Cultivate the ability to interpret results and communicate findings effectively.
6. Learn to use advanced data analysis software and programming languages efficiently.
7. Familiarize with the ethical considerations in data analysis and the importance of data privacy.
Course Prerequisites
– Basic understanding of statistics- Familiarity with programming (Python/R)
– Knowledge of database concepts
– Experience with data manipulation and cleaning
– Grasp of machine learning fundamentals
– Comfort with mathematical concepts such as linear algebra and calculus
Learning Options
Target Audiance
- - Data scientists and analysts seeking to enhance their skills
- - Experienced professionals in IT, finance, research, or business intelligence
- - Managers overseeing data-driven decision-making processes
- - Individuals aiming to transition to roles requiring advanced analytical expertise
- - Academics and researchers using data analysis in their work
Schedule Dates
Advanced Data Analysis Techniques
Advanced Data Analysis Techniques
Advanced Data Analysis Techniques
Advanced Data Analysis Techniques
Course Content
- Consolidating data from separate files and sheets.
- Advanced data validation using lists, dates, and custom validation.
- Powerful array functions.
- Cell management tools: left, right, mid, concatenate, value.
- Naming, editing, and managing cells and ranges.
- Subtotal, Sumif, Sumifs, Sumproduct, Count, Countif, and Countifs for Excel.
- Looking-up to data, Texts, and values using Vlookup in Excel.
- Slicing dates into day names, weeks, week numbers, month names, years, and quarters.
- Text-to-columns and dynamic trimming using trim and len.
- Managing texts and numbers using replace, find, and substitute.
- Text change functions.
- The 20 must-learn pivot-tables tools.
- Creating pivot tables.
- Number formatting techniques.
- Designing report layout.
- Filtering labels and values.
- Expanding and collapsing reports.
- Summarize values by sum, average, minimum, maximum, count.
- Show values as % of total and % of.
- Pivot table options.
- Inserting formulas.
- Creating pivot charts.
- Dynamic chart labeling.
- Mastering the slicer.
- Showing report filter pages.
- Linking pivot tables and pivot graphs with PowerPoint.
- Conditional formatting with pivot tables.
- Development of time series models using histograms and moving averages.
- Exponential smoothing and regression analysis to develop are used data analysis techniques.
- Mastering the use of exponential smoothing as a data analysis tool.
- Validation of time series analysis.
- Appreciate the meaning and importance of sensitivity analysis.
- Developing \"What-if\" scenarios in your financial or operational models.
- Using the Excel tools \"scenario and goal seek.\"
- Charting and visualization techniques.
- Using the camera tool.
- Working with formula-driven visualizations.
- Using fancy fonts.
- Leveraging symbols in formulas.
- Working with sparklines.
- Creating unconventional style charts.
- Controlling and protecting your reports, worksheets, and workbooks.
- Data entry form.
- Custom list.
- Text-to-speech.
- Advanced conditional formatting.
- Shortcuts for your daily work.
- Exercises
- Review course objectives and suggestions
FAQs
The course spans over 4 weeks, with classes held twice a week.
This course is designed for professionals and students who have basic knowledge of data analysis and wish to delve deeper into advanced techniques.
Participants are expected to have a basic understanding of statistics and data analysis concepts. Familiarity with programming languages like Python or R is also beneficial but not mandatory.
The course covers advanced data analysis techniques such as predictive modeling, machine learning algorithms, data visualization, and data mining.
Our instructors are highly experienced professionals with expertise in data analysis and relevant industry experience.
Duration
Language
Batch Options
Upcoming Batch
Soft copy of Courseware
Certificate of Course Complition
Experienced and Professional Trainer
Lab and Exercises During Training 
Sample Questions for Exam Prepration
Mock Test on the Last Day of Training 
Exam Registration Guidance 
5-Star Training Environmment 