Fundamental Big Data Analysis & Science
irna
Wed, 09/28/2022 - 03:57
Duration
3 Days
Exam Details
- Exam Code: B90.04: Fundamental Big Data Analysis & Science
- Duration: 60 minutes
- A passing grade on Exam B90.04 is a requirement for the following certification(s):
- Certified Big Data Scientist
- Certified Big Data Consultant
- Availability: Pearson VUE Testing Centers Worldwide, Pearson VUE Online Proctoring, On-Site Proctoring
Price
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This course provides an in-depth overview of essential topic areas pertaining to data science and analysis techniques relevant and unique to Big Data with an emphasis on how analysis and analytics need to be carried out individually and collectively in support of the distinct characteristics, requirements and challenges associated with Big Data datasets.
Students will be able to know about:
- Exploratory Data Analysis
- Statistics Mathematics, Variable Categories, Analysis, Models
- Visualization
- Big Data Dataset Categories
Objectives
The following primary topics are covered:
- Data Science, Data Mining & Data Modeling
- Big Data Dataset Categories
- High-Volume, High-Velocity, High-Variety, High-Veracity, High-Value Datasets
- Exploratory Data Analysis (EDA)
- EDA Numerical Summaries, Rules and Data Reduction
- EDA analysis types, including Univariate, Bivariate and Multivariate
- Essential Statistics, including Variable Categories and Relevant Mathematics
- Statistics Analysis, including Descriptive, Inferential, Covariance, Hypothesis Testing, etc.
- Measures of Variation or Dispersion, Interquartile Range & Outliers, Z-Score, etc.
- Probability, Frequency, Statistical Estimators, Confidence Interval, etc.
- Data Munging and Machine Learning
- Variables and Basic Mathematical Notations
- Statistical Measures and Statistical Inference
- Confirmatory Data Analysis (CDA)
- CDA Hypothesis Testing, Null Hypothesis, Alternative Hypothesis, Statistical Significance, etc.
- Distributions and Data Processing Techniques
- Data Discretization, Binning and Clustering
- Visualization Techniques, including Bar Graph, Line Graph, Histogram, Frequency Polygons, etc.
- Prediction Linear Regression, Mean Squared Error and Coefficient of Determination R2, etc.
- Clustering k-means, Cluster Distortion, Missing Feature Values, etc.
- Numerical Summaries
Contact
- Nurman (+62 857-2375-3840)
- Irna (+62 822-1664-7749)
- Puji (+62 813-2424-2115)