What Projects Are Included In This Data Science Course In Houston
This Data Science training in Houston features more than 15 real-life, industry-based projects highlighting different domains. These projects help you master concepts of Data Science and Big Data. Here are a few of the projects:
Capstone Project:
Description: Youll go through dedicated mentor classes to generate a high-quality industry project where you solve a real-world problem by leveraging the skills and technologies that you learned throughout the program. The capstone project includes all the key points of data extraction, cleaning, and visualization, and how to build and tune models. You can also choose the domain/industry dataset you want to work on, based on whatever options are available.
After you successfully submit your project, you will earn a capstone certificate, showcasing your expanded learning and skills to potential employers.
Project 1: Products rating prediction for Amazon
Domain: E-commerce
Amazon, one of the leading US-based e-commerce companies, usually recommends products to customers that fall in a similar category that jibes with their activity and reviews. Amazon would like to boost this recommendation engine by increasing its capabilities, letting it predict ratings for non-rated products and adding them accordingly to the customers recommendations.
Project 2: Improving customer experience for Comcast
Domain: Telecom
Project 3: Attrition Analysis for IBM
Domain: Workforce Analytics
Domain: Retail
Domain: HealthCare
Domain: Insurance
Data Cleansing For Big Data
The always increasing accumulation of semi-structured and unstructured data from a pile of sources makes big data vast and complex by nature. These sources can be anything including mobile devices, sensors application servers, GPS systems, etc. Contrasting source of data is translated into an equally contrasting format. Until a data is not transformed into a unified form, data scientists cannot make sense of the data.
The main problem is that logs, as well as metrics, come in different forms, making both analysis and correlation difficult for each point and almost impossible between the two. The data format of metrics is short and it describes measurements beyond the measurable value including location, type, grouping, and time of measurement. Logs, generated by applications or infrastructure, are used to provide the operational team with very specific details that can help them analyze a particular security or operational event. Therefore, they tend to be longer than the metrics and can come in different shape and forms. The developers sometimes define the format of some logs even though they are standardized.
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Data Cleansing Tips & Methods
Now that you know what data cleansing is and why its so important, you may be wondering how you can start the data cleansing process! With data cleansing, there is no one size fits all. Your data cleansing methods will often depend on the type of data you have. However, here are some general tips to help you get started.
Assess Your Data
Data cleansing usually involves cleaning data from a single database, such as a workplace spreadsheet. If your information is already organized into a database or spreadsheet, you can easily assess how much data you have, how easy it is to understand, and what may or may need updating. If your data is currently in individual files and spread across your computer, you will want to compile it all so you can begin assessing it as a whole.
Brendan Bailey from Towards Data Science outlines some questions to ask for initial data assessments, including:
- Does my data seem to make sense?
- Are there any duplicates, and if so, is that okay?
- Does numerical data add up and make sense?
- Are there spelling errors or numbers where there shouldnt be?
This initial assessment can help you get a better grasp of how much you need to do. If you notice all your data is from 2005, you may have your work cut out for you! But if you simply notice a few outdated numbers and a spelling mistake or two, a quick update may be all you need.
Clean Data In A Separate Spreadsheet
Make Use Of Functions
Use Data Cleansing Software
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Prepares Data For Transformation
Before converting raw data from one format to another, data must be free of irrelevant values, errors and duplications. Data cleaning also allows you to make sure you’re converting accurate data sets for analysis. Cleaning data before transformations ensures data warehousing and storage processes operate efficiently.
S For Data Normalization
There are several methods for performing Data Normalization, few of the most popular techniques are as follows :
- The min-max normalization is the simplest of all methods as it converts floating-point feature values from their natural range into a standard range, usually between 0 and 1. It is a good choice when one knows the approximate upper and lower bounds on the data with few or no outliers and the data are approximately uniformly distributed across that range.
- Z-score normalization is a methodology of normalizing the data and hence helps avoid the issue of outliers in the data. Here, is considered the mean value of the feature and is the standard deviation from the data points. If a value comes to be equal to the mean of all the values present, only then will it be normalized to 0. If it is below the mean value, it will be considered to be a negative number, and if it is above the mean value it will be termed as a positive number. The number of the negative and positives are determined by the standard deviation of the original feature.
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Useful Data Mining Applications
Many stand-alone applications, like OpenRefine, which uses a spreadsheet metaphor and formulae to transform data, further reduces the code burden for operators. WinPure Clean & Match breaks down the process of cleansing data into specific sections seven in total allowing users to focus on each in turn while promising to be easy to use, even without specialized training.
Data Cleaning: Definition Importance And How To Do It
In data analysis, statistics and technology fields, data cleaning is essential for ensuring the accuracy and validity of compiled data. Before you upload data for warehousing and analysis, cleaning sorts and organizes raw data so that businesses can interpret important information more easily. In many technical applications, data cleaning is crucial for supporting businesses and organizations in the storage and use of accurate data. In this article, we explore what data cleaning is, why it’s important and how to clean data with some tools and resources that can be useful in this process.
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What Is Data Cleansing And Why Is It Important
Data cleansing or cleaning refers to removing inaccurate and irrelevant data. Sometimes, it is also known as data scrubbing. As this term suggests, cleansing is all about removing inconsistent and invalid data. Primarily, this process targets typographical errors, null values or blanks and duplicates.
But, it cannot be possible without screening anomalies. This is where data validation comes into play. Data cleansing companies check accuracy and quality of data prior to importing and data processing. This process mainly filters blank and null values, unique values and a range of consistent data. This is how a refined version of data shapes up.
What Is Data Quality
Data quality is the qualitative and or quantitative measure of how well our data suits the purpose it is required to serve. These measures are practical templates we can use to assess how suitable the data provided is for our desired purposes. Consider these data quality measures as metrical tests our data must pass to deem it fit for data science processes.
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Remove Duplicate Or Irrelevant Observations
Remove unwanted observations from your dataset, including duplicate observations or irrelevant observations. Duplicate observations will happen most often during data collection. When you combine data sets from multiple places, scrape data, or receive data from clients or multiple departments, there are opportunities to create duplicate data. De-duplication is one of the largest areas to be considered in this process. Irrelevant observations are when you notice observations that do not fit into the specific problem you are trying to analyze. For example, if you want to analyze data regarding millennial customers, but your dataset includes older generations, you might remove those irrelevant observations. This can make analysis more efficient and minimize distraction from your primary targetas well as creating a more manageable and more performant dataset.
Data Cleaning And Migration Why Its Essential In Every Business
With the importance of data quality across various industries, you probably have done some data cleaning within your database.
Its purpose is simple to determine inaccurate data and improve the quality through correcting detected errors. However, what you know now about data cleaning is just the tip of the iceberg. For sure right now, you must be wondering what is data cleaning, and why does my business need it?
As an operating enterprise, youre capable of processing and storing huge amounts of customer data. But as time goes by, your customers tend to change details in their personal information such as addresses and contact numbers.
For this, you might need to constantly update your data to reduce all kinds of data entry errors. Otherwise, these errors will transform data into inaccurate information that may affect your sales conversion, as well as your customer satisfaction.
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Improve Process Efficiency And Productivity
Cluttered databases lead to a decrease in productivity. Computers take longer to pull information. Client menus become filled with past clients, forcing the office administrator to go through a larger list to put in an order. Or worse, managers place order with older suppliers who are no longer contracted with the business. All of these things can easily happen when data starts to get cluttered.
Businesses decide to outsource data cleansing services when things get so far out of hand that it begins to cause significant delays. Dont wait. Develop a plan today.
Why Get Certified As A Data Scientist
A question that I often hear from clients and colleagues is, “Why should I get a Data Science certification?” That is a fair question for most other areas of study and business. In areas such as finance or engineering, there are far more important accreditations you could and should achieve before hanging your shingle or trying to retool your skill set or career.
Data science is a broad discipline with a few accredited cetification programs. However, many of those programs are cost-prohibitive.
There are at least 50 Data Science certification programs by universities worldwide offering degree and diplomas in this discipline, writes data science blogger, Zeeshan Usman. It costs from $50,000 to $270,000 and takes one to four years of your life.
And although somewhat new in the nomenclature, data science encompasses many skills that professionals may already have acquired through work or educational experience such as:
A Fresh Perspective
Furthermore, Data Science certifications allow students to learn and hone skills that wont normally be acquired through work experiences, such as exploratory analysis skills, visualization skills, and data mining/machine learning algorithms.
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Save Time And Increase Productivity
How many hours per month are wasted by sales and marketing teams on calls and emails to expired contacts or people who simply arent interested?Probably more than you would like to admit.
More accurate data reduces the time wasted contacting invalid prospects and customers by phone or email. By maintaining a quality data set you can boost the productivity of staff and positively impact the business as a whole.
What Are Some Reliable And Free Data Sources
One of the methods to collect data is from websites, getting their raw text data and then cleaning it. Another way is to get data from websites like Kaggle, UCI Machine Learning repository and official government websites. A very useful article here contains a comprehensive list of dataset. Additional websites include –
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Integrated Systems Are Not Immune Either
Entering data into any system can sometimes be tiresome. This naturally creates impatience and haste. It might be good enough then and there, but later the data might be insufficient for use elsewhere in the company.
It may not immediately be a problem. When the data entry shows up next in another department, they will just add the missing pieces, or correct the faulty data.
However, it can also mean that they WONT find the entry.
Because they THINK the data doesnt exist, they enter it again, creating duplication.
Key Benefits Of Data Cleaning
As weve covered, data analysis requires effectively cleaned data to produce accurate and trustworthy insights. But clean data has a range of other benefits, too:
Key to data cleaning is the concept of data quality. Data quality measures the objective and subjective suitability of any dataset for its intended purpose. There are a number of characteristics that affect the quality of data including accuracy, completeness, consistency, timeliness, validity, and uniqueness. You can .
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Cleaning Your Data Is A Must
Businesses that take proper care of their databases are rewarded with these and many more benefits. Organizations that keep business critical information at a high-quality gain a significant competitive advantage in their markets because theyâre able to adjust their operations to the changing circumstances quickly.
At Sunscrapers, we know that clean data is the starting point for any successful data science project, especially for building sophisticated solutions like machine learning algorithms. We always take proper care to clean data and make sure that our projects bring maximum benefits to our clients and their data management practices.
Are you looking for more information about data cleaning and more data-related topics? Follow our company blog where our experts share their knowledge about data science with our community.
Data Cleaning Importance And Benefits
The importance of clean data, as mentioned, crosses boundaries. Figures show that the US economy drains at least $3 trillion per year through dirty data management.
However, the importance of clean data is more than an economic concern. Here are a few of the key benefits of cleaning data on a wide scale.
- Cleaning siloes and data lakes can help to remove errors.
- Fewer errors can result in boosts to efficiency and productivity.
- Higher quality data can also ensure higher quality standards of customer care.
- Cleanup can help to set up efficient data maintenance for the future.
- Cleaner data is easier to pinpoint should problems arise in the future.
- Cleaning up can help companies set up more precise business roadmaps and funnels.
- Data cleaning can also help to prevent bottlenecks in service delivery.
Of course, the examples above apply to the broadest spectrum. There are specific cases across industries and businesses where cleanup may be of further benefit.
For example, in healthcare, clearer, more concise records can help speed up patient diagnosis. This data can also ensure more effective medication and treatment precision. As everyone will need a medical record, cleanup can also plan for growing data.
In the banking sector, clean data is equally important. Data cleansing can help to fight against fraud. It can also help to safeguard wealth, as well as to ensure customers receive relevant support.
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How Clustering Is Handled During Data Mining
These are far from the only options and a full range of complimentary tools, algorithms and processes can be applied in sequence so that each improves on the output of that which it follows. Others, like hierarchical clustering, can be applied in different ways, depending on the required output. Bottom-up clustering, for example, can be used to incorporate diverse data points into an increasingly homogeneous entity should that be required, while a similar process, in reverse, can break out individual groups from a single data pool into more granular subsets if approached from top-down.
In the above example, the bottom-up approach would allow the organization to find which members of a group, like an identified set of potential customers, are more likely to respond to specific prompts. So, if it held detailed demographic data on 10,000 prospects, and a budget to produce five pieces of marketing material, it could assign each of its 10,000 leads to one of five groups with broadly similar characteristics and develop material to target each one.
Alternatively, if the material has already been produced, it may take the top-down approach, using characteristics of the five marketing campaigns to break down its 10,000-strong customer database to identify which cohorts are most likely to respond positively to the assets it already has to hand.
Why Be A Data Scientist
Data is everywhere. We use it at work, at home, or when conducting online commerce, and more is generated every day. So, Data Scientists are consequently the highest ranked professionals in any analytics organization. Glassdoor ranks the career of Data Scientist second in the 50 Best Jobs for 2021. Theres a shortage of Data Scientists, so thats why its a great idea to take this data science course in Chicago. An expert Data Scientist understands the requirement and constraints of business problems, collects the right data and makes it usable to design the right analytical strategies, apply the most effective techniques or algorithms to come up with actionable insights for implementation.
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