Fundamentals of Data Analytics and Methods Course by Tonex
Data Analytics refers to the set of quantitative and qualitative approaches for deriving valuable insights from data. It involves many processes that include extracting data and categorizing it in order to derive various patterns, relations, connections, and other such valuable insights from it.
Data analytics involves applying an algorithmic or mechanical process to derive insights. For example, running through a number of data sets to look for meaningful correlations between each other.
Data analytics is a general term for any type of processing that looks at historical data over time, but as the size of organizational data grows, the term data analytics is evolving to favor big data-capable systems.
Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms and what-if analysis powered by high-performance analytics systems.
One of the most important tasks in big data analytics is statistical modeling, meaning supervised and unsupervised classification or regression problems. Once the data is cleaned and preprocessed, available for modeling, care is taken in evaluating different models with reasonable loss metrics and then once the model is implemented, further evaluation and results should be reported.
Big data analytics is used in a number of industries to allow organizations and companies to make better decisions as well as verify and disprove existing theories or models.
The focus of data analytics lies in inference, which is the process of deriving conclusions that are solely based on what the researcher already knows.
Many believe there has never been a more exciting time to work with data.
The data analytics industry is predicted to reach almost $78 billion by 2023, and in 2020, there will be an estimated 2.7 million jobs open for data analysts and data scientists.
Fundamentals of Data Analytics and Methods Course by Tonex
Fundamentals of Data Analytics and Methods is a 3-day provides participants the knowledge and skills to understand the data analytics and associated methods. Data Analysis is a discipline focusing on big data, data analytical methodologies and tools, business intelligence, effective decision-making, new predictive modeling techniques coupled with rich analytical, techniques for qualifications for the data analyst, and visualization tools to manage big data.
Fundamentals data analytics and methods teaches participants about data analytics, data tools, big data and visualization strategies to maximize the value of available information.
This course help you make sense of data analytics and data engineering and derive actionable recommendations.
Audience
Fundamentals of Data Analytics and Methods is a 3-day course designed for:
- Product design managers, engineers, or quality managers
- IT and Business Intelligence professionals
- Personnel involved with product quality assurance and regulatory compliance testing
- Engineers to manipulate the data, create data visualizations and make strategic predictions
- Mangers and individuals dealing with uncertainty in their products or processes
- Faculty members who want to teach the engineering statistics or data analysis course
Learning Objectives
Upon the completion of Mathematics and data analysis/Engineering statistics /Statistical Data reduction, the attendees can to:
- Learn the basic concepts in business intelligence and data analytics and statistics
- Explain different types of data, data sources and the application of data analysis
- Describe the process of creating an data analysis plan with alternative analytic purposes (e.g., explanatory versus confirmatory)
- Define the key data types (nominal, ordinal, interval, ratio, etc.)
- Lean the methods to represent different types of data and data analysis
- Describe the procedures in statistical data reduction
- Explain the different between descriptive and inferential statistics
- Differentiate the different types of distributions in statistics
- Learn about Forecasting, including introduction to simple Linear Regression analysis and exponential smoothing method
- Learn about descriptive and visual approaches used with familiarization of the data
Training Outline
Introduction to Data and Data Sources
- The world map of big data tools
- What is Data Analytics?
- Why is Data Analytics important?
- Qualitative data
- Quantitative data
- Distribution shapes
- Discrete and continuous data
- Analog and digital data
- Data Analytics tools
- Role of Data analysts
- Data warehouse
- Relational databases
- Data manipulation
- Entity Resolution and De-Duplication
- Data Imputation
- Natural Language Processing (NLP)
Representing the Data
- Bar graphs
- Pie charts
- Dot plots
- Line graphs
- Scatter plots
- Pictograph
- Histograms
- Frequency distribution
- Cumulative tables
Types and Sources of Data
- Qualitative data
- Quantitative data
- Distribution shapes
- Discrete and continuous data
- Analog and digital data
- Traditional databases
- Modern data sources
Representing the Data
- Data visualization
- Bar graphs
- Pie charts
- Dot plots
- Line graphs
- Scatter plots
- Pictograph
- Histograms
- Frequency distribution
- Cumulative tables
- Natural language processing
- Machine languages
- Supervised and unsupervised methods
- Machine Learning (ML) and Deep Learning (DL)
Data Analysis
- Organizing data
- Analyzing quantitative data
- Mean, median, mode and range
- Inferential analysis
- Statistical significance
- Clinical significance
- Analyzing qualitative data
- Interpreting the results
Data Preparation
- Data Location, Acquisition, and Ingestion
- Data Assessment and Cleaning
- Data from transactional (administrative) systems
- Data Uniformity and Reconciliation
- Data Coding and Annotation
- Data Currency and Refresh
- Data Preparation Methods
Data Reduction
- Measures of location/ measures of central tendency
- Mean versus expected value
- Properties of the mean
- Measures of dispersion
- Properties of variance
- Standard deviation
- Coefficient of variation
- Measures of association
- Covariance
- Correlation coefficient
Introduction to Statistics
- Definition
- Population
- Data and variables
- Class intervals
- Descriptive statistics
- Inferential statistics
- Sampling and regression
- Exponential smoothing
Descriptive Statistics
- Organizing quantitative and qualitative data
- Measure of center
- Concept of mean, mode and median
- Measure of variation
- Descriptive measure for populations
Inferential Statistics
- Estimation of population mean
- Confidence intervals
- Margin of error
- Hypothesis testing
- Critical value approach
- P-value approach
- Inference for two population means
- Sampling distribution of two sample means
- Mann-Whitney test
- Inference for population standard deviation
- Inference for population proportions
Probability and Random Variables
- Basics of probability
- Events
- Rules of probability
- Contingency tables
- Conditional probability
- Theory of intersection
- Rules of addition
- The multiplication rule and independence
- Bayes’s rule
- Test statistics
- Counting rule
- Discrete random variables and probability distribution
- Mean and standard deviation of a random variable
Distributions
- Binomial distribution
- Continuous distribution
- Normal distribution
- Gaussian distribution
- Statistical significance
- Confidence intervals
- Chi-Square distribution
- F-distribution
- Poisson distribution
Correlation and Regression
- Linear equations with one independent variable
- The regression equation
- Linear correlation
- Regression model
- Estimation and prediction
- Inferences in correlation
- Analysis of variance (ANOVA)
- One-way ANOVA
Descriptive and Exploratory Analysis
- Classification
- Regression tree analysis
- Data Mining
- Text Analytics
- Bayesian Methods
- Simulation
- The visualization or presentation
- Predictive Analysis
- Machine Learning
- Machine learning methods
- Supervised
- Unsupervised
- Reinforcement
- Linear Regression
- Nonlinear Regression
Working with Tools in Data Analytics
- Apache Spark
- KNIME
- Microsoft Excel
- OpenRefine
- Python
- QlikView –
- R programming
- RapidMiner
- SAS
- Tableau Public
Example of Open APIs
- Hadoop
- HDFS
- MapReduce
- HBase
- SPARK
- YARN
- PIG
- HIVE
- OOZIE
- FLUME
Fundamentals of Data Analytics and Methods Course by Tonex