What is data analysis PPT?

What is data analysis PPT?

Data Analysis  The purpose  To answer the research questions and to help determine the trends and relationships among the variables.  Descriptive Statistics  are numerical values obtained from the sample that gives meaning to the data collected.

How do you present data analysis in PowerPoint?

1. Consider your options. First, it’s important just to know what your options are for presenting data.
2. Go beyond PowerPoint. Above, we have outlined the most basic methods for presenting data.
3. Mix it up.
4. Keep it simple.
5. Be original.
6. Use images.
7. Highlight the important stuff.

How do you do a data analysis presentation?

Here are my 10 tips for presenting data:

1. Recognize that presentation matters.
2. Don’t scare people with numbers.
3. Maximize the data pixel ratio.
4. Save 3D for the movies.
5. Friends don’t let friends use pie charts.
6. Choose the appropriate chart.
7. Don’t mix chart types for no reason.
8. Don’t use axes to mislead.

What are data presentation techniques?

Some of the popular ways of presenting the data includes Line graph, column chart, box pot, vertical bar, scatter plot. These and other types are explain below with brief information about their application.

What are the data presentation tools?

Data Visualization Tools Comparison

• Tableau (and Tableau Public) Tableau has a variety of options available, including a desktop app, server and hosted online versions, and a free public option.
• Infogram.
• ChartBlocks.
• Datawrapper.
• D3.
• Google Charts.
• FusionCharts.
• Chart.

What is data Visualisation tool?

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

What is Kibana tool?

Kibana is an open-source data visualization and exploration tool used for log and time-series analytics, application monitoring, and operational intelligence use cases. It offers powerful and easy-to-use features such as histograms, line graphs, pie charts, heat maps, and built-in geospatial support.

What are the types of data presentation?

Graph presentation

• Scatter plot. Scatter plots present data on the x- and y-axes and are used to investigate an association between two variables.
• Pie chart.
• Three-dimensional effects.

What is data preparation process?

Data preparation is the process of cleaning and transforming raw data prior to processing and analysis. For example, the data preparation process usually includes standardizing data formats, enriching source data, and/or removing outliers.

What is the process of data cleaning?

Data cleaning is the process of fixing or removing incorrect, corrupted, incorrectly formatted, duplicate, or incomplete data within a dataset. When combining multiple data sources, there are many opportunities for data to be duplicated or mislabeled.

What are the two types activities in data preparation?

There are variations in the steps listed by different data preparation vendors and data professionals, but the process typically involves the following tasks:

• Data collection.
• Data discovery and profiling.
• Data cleansing.
• Data structuring.
• Data transformation and enrichment.
• Data validation and publishing.

Why is data preparation important?

Data preparation ensures accuracy in the data, which leads to accurate insights. Without data preparation, it’s possible that insights will be off due to junk data, an overlooked calibration issue, or an easily fixed discrepancy between datasets.

What is the role of data in machine learning?

Machine learning models will generally contain a few different datasets, each used to fulfill various roles in the system. The more data you provide to the ML system, the faster that model can learn and improve.

How data is used in machine learning?

Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Deep learning is a specialized form of machine learning.

What is data in machine learning?

DATA : It can be any unprocessed fact, value, text, sound or picture that is not being interpreted and analyzed. Data is the most important part of all Data Analytics, Machine Learning, Artificial Intelligence. KNOWLEDGE : Combination of inferred information, experiences, learning and insights.

Which type of data is required in machine learning?

You also need to convert data types of some variables in order to make appropriate choices for visual encodings in data visualization and storytelling. Most data can be categorized into 4 basic types from a Machine Learning perspective: numerical data, categorical data, time-series data, and text.

Which data is used to teach a machine learning algorithm?

Answer. The data type used is training data. Machine learning refers to the investigation of PC calculations that improve consequently through experience.

What are the four types of machine learning?

The types of machine learning algorithms are mainly divided into four categories: Supervised learning, Un-supervised learning, Semi-supervised learning, and Reinforcement learning.

What are the machine learning techniques?

5 Essential Machine Learning Techniques

• Regression. Regression methods are used for training supervised ML.
• Classification. Classification algorithms can explain or predict a class value.
• Clustering. Clustering algorithms are unsupervised learning methods.
• Decision Trees.
• Neural Networks.

How can I learn algorithm?

1. Step 1: Learn the fundamental data structures and algorithms. First, pick a favorite language to focus on and stick with it.
2. Step 2: Learn advanced concepts, data structures, and algorithms.
3. Step 1+2: Practice.
4. Step 3: Lots of reading + writing.
5. Step 4: Contribute to open-source projects.
6. Step 5: Take a break.

Which algorithm is best for prediction?

Naive Bayes is a simple but surprisingly powerful algorithm for predictive modeling. The model is comprised of two types of probabilities that can be calculated directly from your training data: 1) The probability of each class; and 2) The conditional probability for each class given each x value.

What is basic machine learning algorithm?

Major focus on commonly used machine learning algorithms. Algorithms covered- Linear regression, logistic regression, Naive Bayes, kNN, Random forest, etc.