advanced analytics course - Finance, Tech & Analytics Career Resources

The Impact of Data Science on Various Industries

Posted on July 20, 2023January 22, 2024 by Imarticus Learning

Data Science and Industrial Integration

Data science studies the extraction and derivation of meaningful insights from data. The approach to gathering the insight is multidisciplinary.

It takes practices and principles from a variety of fields. Some of these fields are computer engineering, statistics, artificial intelligence, and mathematics.

The applications of data science are unlimited as it answers the four important questions of problem-solving. These are what is the situation, why is it, what is the future course, and what would the result yield.

This basic information gathering and problem-solving make the adoption of data science in different industries feasible.

Data science finds its application in industries such as retail, medicine, banking, finance, and telecommunications among others. Read ahead if you are planning to make a career in data science and are curious about the impact on various industries.

Impact of Data Science on various industries

A career in data science requires professionals to make use of raw data for the identification of patterns. These patterns are then used to derive actionable insights from the data.

With data science, it is possible to predict future outcomes with a higher level of accuracy. Here is how various industries are benefiting from the applications of data science.

1. Healthcare

Applications of data science in the healthcare industry include the use of data in taking important decisions and drawing conclusions. These include the implementation of medical knowledge gathered in the diagnosis of diseases.

Data science is also used to improve the safety and quality of healthcare by helping design prevention plans. It has also helped chart important health parameters such as sugar level, stress level, brain activity, etc using trackers. This can help deliver personalised care and precise prescriptions. A deep learning technique is used in reading imaging data. This is also to reduce diagnostic failures.

2. Retail

In the retail industry applications of data science have led to the implementation of features such as recommendation engines. The impact helps companies design products according to the needs of the consumer. Market-based analysis allows the company to determine the likes, dislikes, and price points of consumers. This allows companies to target the right audience more efficiently.

The science has also helped the industry to analyze customer sentiments more accurately. Social media and other points of contact between consumers and companies help companies collect data. This is further used to enhance the feedback mechanism. The feedback is then used to design better products. Firms using data science can optimise prices efficiently. All these lead to an increase in sales and revenue flow in the industry.

3. Banking, Finance Services, and Insurance Industry (BFSI)

BFSI has benefitted largely from the application of data science in the industry. The industry has been able to minimize its losses with the help of fraud detection using science. It has impacted more efficient management of customer data, risk modelling, and customer support.

Data scientists can generate customer life value prediction that is used to recommend products and services to consumers as per their needs. BFSI companies are also able to minimize the impact of any risk to their businesses with the help of predictive and real-time analysis. These techniques have led firms to increase sales and operational gains.

4. Telecommunication Industry

If you are looking for a career in data science in the telecommunication industry you will be able to impact efficient data transmission and visualisation needs. Data science is applied for increasing fraud detection by firms. Using science, consumers benefit from increased network security.

The benefits for the company include price optimisation, real-time analysis, and prevention of customer churn. Companies can also benefit from features such as location-based promotion, predictive analysis, and targeted marketing.

5. The course you need for a career in data science

The example of the impact of data science on four industries is a mirror of the significance of data science in today’s world. If you are looking for a career in data science Imarticus Learning’s Postgraduate Program In Data Science And Analytics is the one.

The course is designed for early career professionals. This six-month program is designed based on a job-specific curriculum. The learning is accentuated by live learning modules, real-world projects, and KPMG hackathons. The certification is designed to launch your career in data science in an industry of your choice.

Summing it up

Applications of data science are wide. There are several industries apart from healthcare, retail, BFSI, and telecom that are using data science. Data science can help companies grow and the skills of a data scientist are always in demand. You can benefit from a quick start in your career by being a part of Learning’s Postgraduate Program In Data Science And Analytics.

Imaritcus Learning offers you benefits such as Job-assurance, specialized training, and placement drive. It has helped more than 56000 individuals attain placements. Be sure to visit us for other similar courses.

Why Learning Python Is Essential For Data Science

Posted on July 11, 2023April 5, 2024 by Imarticus Learning

Python is a versatile programming language. When you learn Python, the programming language makes you competent for several job roles.

It finds applications in Data Science, Web Development, Analytics, Automation, Scripting, and Game Development.

Why is it so widely used?

Let’s explore the reasons to learn Python in the following sections.

What are the technical requirements of the Data Science industry?

Data Science and Analytics industry is based on mathematics and statistics. As a Data Scientist, technically, you will need some assistance to complete your job. These are listed below.

Fundamentals: You must know and learn Python programming language. Additionally, SQL is essential for understanding querying and database management.
Mathematics and Statistics: Creating reliable models is the main responsibility of a Data Scientist. This can only be done with sound knowledge of probability, regression analysis, hypothesis testing, and linear algebra.
Data Analysis and Manipulation: A Data Scientist performs data manipulation and analysis regularly. The programming language used for this job must help with feature engineering and data cleaning.
Big Data Technologies: Along with the fundamentals, you must learn frameworks. Apache Hadoop, Dask, or Apache Spark frameworks efficiently analyze and process massive databases.
Machine Learning: A Data Scientist must know machine learning algorithms. They help in performing regression, clustering, and classification.

Why should Data Scientists learn Python over other languages?

When you learn Python for Data Science and Analytics, each lesson will speak of the language’s relevance in the field.

Listed below are the reasons that make Python important for Data Scientists.

If you’re wondering the reason for choosing Python over other languages, keep reading.

Simplicity: When compared with C, C++, or C#, Python is easy to learn. Data Scientists can learn the language easily and start coding in no time. The syntax of Python is simple and almost in the English language.

This enables you to focus on problem-solving instead of bothering yourself with the format of a new language. It does not just help you with the process, but it also enables teamwork. This means you can collaborate with others to complete a project on time.

Library: Python is popular for its vast library. When you talk about Data Science, the language offers one of the largest collections of frameworks and libraries. These are specific to Data Science and Analytics. For example, NumPy, Matplotlib, and Pandas are important libraries.

Support for Artificial Intelligence (AI): Tensorflow is one of the most useful libraries in Python for AI. The language also offers other frameworks and libraries. You can use these for building and training complex models. With Python, you can also integrate machine learning pipelines directly into the production systems.

Integration: In the previous section, you must’ve learned the importance of Big Data Technologies, SQL, and Visualization Tools. Python is compatible with SQL, Hadoop, and Tableau to enhance your capabilities as a Data Scientist. You can also integrate your Python code with other programming languages.

These reasons compel professionals to learn Python for Data Science and Analytics. However, you must note that programs like R, MATLAB, and Julia also help in Data Science. But, the simplicity of Python and its vast library has made it the top choice of professionals.

Join a job-assured Postgraduate Data Science & Analytics Course

As Python is lightweight, with extensive libraries, and customer support, the demand for this program is not getting down anytime sooner. When you become a data science expert, you must have a clear understanding of the fundamentals. Strengthen your concepts of mathematics and statistics and learn machine learning.

A Postgraduate Data Science and Analytics Course will direct your career in the right direction. Imarticus Learning presents a job-centric curriculum to make the most of your investment. You will also get assistance for participating in Hackathons that add value to your resume. Get ahead of your competitors by starting your journey today!

Data Cleaning and Preprocessing: Ensuring Data Quality

Posted on June 26, 2023April 2, 2024 by Imarticus Learning

Data cleaning and preprocessing are crucial phases in data analysis that entail changing raw data into a more intelligible, usable, and efficient format. Data cleaning is repairing or deleting inaccurate, corrupted, improperly formatted, duplicate, or incomplete data inside a dataset. On the other hand, data preprocessing comprises adding missing data and correcting, fixing, or eliminating inaccurate or unnecessary data from a dataset. Enrolling in a comprehensive data science course with placement assistance helps one to enhance Power BI or Python programming skills and establish a successful career in data analytics.

By spending time and effort in data cleaning and preprocessing, firms can lower the risk of making wrong judgements based on faulty data. This ensures that their analyses and models are based on accurate and trustworthy information. Let’s get detailed insights from this blog.

Role in ensuring data quality and accuracy

Ensuring data quality and accuracy is critical for enterprises to make informed decisions and prevent costly mistakes. Here are several methods and recommended practices to maintain data quality:

Identify data quality aspects: Data quality is judged based on factors such as correctness, completeness, consistency, reliability, and if it’s up to date.
Assign data stewards: Data stewards are responsible for ensuring the data accuracy and quality on stated data sets.
Management of incoming data: Inaccurate data usually comes through data receiving. Thus, it’s essential to have complete data profiling and surveillance.
Gather correct info requirements: Satisfying the needs and providing the data to customers and users for the purpose the data is meant is a crucial component of having good data quality.
Monitor and analyse data quality: Continuously watching and assessing data quality is essential to ensure it fits the organisation’s needs and is correct and trustworthy.
Use data quality control tools: Different tools are available to monitor and measure the quality of data that users input into corporate systems.

Identifying and handling missing data

Identifying irregular data patterns and discrepancies is a crucial part of data cleaning. Inconsistent data can impede pivot tables, machine learning models, and specialised calculations. Here are some tips for identifying and correcting inconsistent data:

To make it simple to spot the incorrect values, use a filter that displays all of the distinct values in a column.
Find patterns or anomalies in the data that can point to errors or inconsistencies.
Find the cause of the inconsistencies, which needs more investigation or source validation.
Create and implement plans to address any disparities and prevent them in the future.

Inaccuracies in data collection, measurement, research design, replication, statistical analysis, analytical decisions, citation bias, publication, and other factors can all lead to inconsistent results. It is crucial to correctly analyse and compare data from various sources to find contradictions.

Techniques for identifying missing data

Here are some techniques for identifying missing data:

Check for null or NaN (Not a Number) values in the dataset.
Look for trends in the missing data, such as missing values in specific columns or rows.
Use summary statistics to locate missing data, such as the count of non-null values in each column.
Visualise the data to discover missing deals, such as heatmaps or scatterplots.
Use data cleansing and management techniques, such as Stata’s mvdecode function, to locate missing data.
Discuss how to address missing data with those who will undertake data analysis.

Benefits and limitations of automation in data cleaning processes

Benefits of automation in data cleaning processes:

Efficiency: Automation can minimise the burden and save time since cleaning can be time-consuming and unpleasant.
Consistency: Automated data cleaning assures reliable findings by applying the same cleaning techniques across all data sets.
Scalability: Automated data cleansing can handle massive amounts of data and be scaled up or down as needed.
Accuracy: Automation can decrease human error by swiftly finding and rectifying problems using automated data cleansing. Minimising human participation in data-collecting procedures ensures that data is inherently more high-quality and error-free.
Real-time insights: Automation can deliver real-time insights and more accurate analytics.

Limitations of automation in data cleaning processes:

Lack of control and transparency: Automated data cleaning methods could have various disadvantages, such as the lack of control and transparency when depending on black-box algorithms and established rules.
Not all data issues can be resolved automatically: User intervention can still be essential.
Over-reliance on automation can be a restriction, as automated solutions are not meant to replace human supervision.
Expensive tooling: A drawback of automated cleaning is that the right equipment could be costly.

Overview of tools and software for data cleaning and preprocessing

Data scientists are estimated to spend 80 to 90 % of their time cleaning data. Numerous industry solutions are accessible to speed up data cleansing, which can be valuable for beginners. Here are some of the best data-cleaning tools and software:

OpenRefine: A user-friendly GUI (graphical user interface) application that allows users to investigate and tidy data effortlessly without programming.
Trifacta: A data preparation tool that provides a visual interface for cleaning and manipulating data.
Tibco Clarity: A data quality tool that can assist in finding and rectifying data mistakes and inconsistencies.
RingLead: A data purification tool that can assist in finding and removing duplicates in the data.
Talend: An open-source data integration tool that can aid with data cleansing and preparation.
Paxata: A self-service data preparation tool that can help automate data cleansing activities.
Cloudingo: A data purification tool that can assist in finding and eliminating duplicates in the data.
Tableau Prep: A data preparation tool that gives visible and direct ways to integrate and clean the data.

How to ensure data quality in data cleaning and preprocessing?

Here are some steps to ensure data quality in data cleaning and preprocessing:

Monitor mistakes and maintain a record of patterns where most errors come from.
Use automated regression testing with detailed data comparisons to ensure excellent data quality consistently.
Cross-check matching data points and ensure the data is regularly formatted and suitably clean for needs.
Normalise the data by putting it into a language that computers can comprehend for optimal analysis.

Conclusion

Data cleaning and preprocessing are crucial in the significant data era, as businesses acquire and analyse massive volumes of data from various sources. The demand for efficient data cleaning and preprocessing methods has expanded along with data available from multiple sources, including social media, IoT devices, and online transactions.

Imarticus Learning offers a Postgraduate Program in Data Science and Analytics designed for recent graduates and professionals who want to develop a successful career in data analytics. This data science course with placement covers several topics, including Python programming, SQL, Data Analytics, Machine Learning, Power BI, and Tableau. The machine learning certification course aims to educate students with the skills and information they need to become data analysts and work in data science. Check the website for further details.

What is Business Analytics All About?

Posted on January 3, 2022April 11, 2024 by Imarticus Learning

Business Analytics Definition

The importance of Business Analytics stems from the fact that it is the method by which firms analyze historical data using statistical methods and techniques to generate new insights and enhance tactical decision-making.

Since data-driven firms see their data as a business asset and actively seek methods to transform it into a competitive advantage, an increasing number of employees are taking data analytics and machine learning courses and acquiring a business analytics certification.

Data quality, trained analysts who understand the technology and the business, and a dedication to leveraging data to uncover insights that influence business choices are all essential components of business analytics success.

What is Business Analytics?

Business analytics is a data managing solution and a subset of business intelligence that involves analyzing and transforming data into valuable information, identifying and predicting outcomes and trends, and making better, data-driven business choices using methodologies such as data mining, predictive analytics, and statistical analysis.

The key elements of a conventional business analytics dash are as follows:

Data Visualization: For easy and rapid data analysis, visual representations such as charts and graphs are offered.
Optimization: after identifying patterns and making forecasts, firms may use simulation tools to test best-case scenarios.
Predictive Analytics: predicting business analytics uses a number of statistical approaches to building predictive models that extract data from datasets, discover trends, and offer a score for a variety of organizational results.
Forecasting: examines historical data from a given time period to make educated predictions about future occurrences or behaviors.
Association and Sequence Identification: identifying predictable behaviors that are conducted concurrently or sequentially with other acts
Text Mining: examines and organizes huge, unstructured text collections for quantitative and qualitative analysis
Data mining for business analytics: data mining for business analytics sifts through large datasets to uncover patterns and connections using databases, statistics, and machine learning.
Data Aggregation: data must be acquired, structured, and filtered before being analyzed, whether through provided transactional records or data.

Why is business analytics important?

Business analytics has a lot of moving pieces, but it’s not always evident why it’s vital to your company. To begin with, business analytics is the instrument that your company requires in order to make informed judgments. These decisions are likely to have an impact throughout your whole organization, assisting you in increasing profitability, market share, and possible shareholder returns.

There’s no doubting that technology has an influence on many enterprises, but when utilized appropriately, BA may have a beneficial impact on your business by giving you a competitive advantage in a variety of ways.

While some firms are unclear what to do with vast volumes of data, business analytics combines data with actionable insights to help you make better business decisions.

Furthermore, because this data may be provided in any manner, your organization’s decision-makers will be well-informed in a way that suits them and the objectives you set at the start of the process.