Uncovering Hidden Insights: Advanced Techniques for Data Analysis

data analytics techniques

Data science courses are so popular these days because of the increasing demand for data analysts and scientists in every business. The data analytics market is expected to hit $103 billion by 2023. Companies produce large amounts of data every day. However, making the right use of such big data becomes challenging due to the lack of skills required to manage this data.

This is where advanced data analytics techniques are very effective. Studies reveal that data analytics solutions can help businesses reduce the cost of failure by 35% when implemented in the right manner. Data analytics plays an important role in making successful business strategies and fastening processes.

What is Advanced-Data Analytics?

Advanced-Data Analytics is a method of automating business processes using data science techniques, such as machine learning, deep learning, predictive modelling, etc. It helps an organization improve efficiency using data assets and solve complex business problems more effectively.

With data science usage beyond traditional BI (Business Intelligence), data scientists use advanced analytics techniques to predict patterns and the probability of future events. Here are some most significant benefits of data analytics in a business:

  • A greater level of accuracy in predictions and forecast models.
  • With accurate predictions, decision-making becomes faster.
  • You get deeper insights with data, such as market trends, consumer preferences, etc.
  • Reduce the risk of expensive mistakes.
  • Determine potential problems of a business and come up with effective solutions.
  • Identify new business opportunities.

Advanced-Data Analysis Techniques

Data Analysis techniques refer to the different methods of analysing a set of data. The following are some of the most effective advanced techniques for data analysis:

Machine Learning algorithms

Machine Learning, a part of Artificial Intelligence, is used to automate learning in machines using past data and experiences. It is used to create statistical models that can produce relevant results from data without much human interference. The best use case of machine learning is the face recognition feature on smartphones. 

Moreover, recommendation engines powered by Machine Learning can bring impactful results to a business. A leading e-commerce company reported a 35% increase in its sales with ML-based recommendation systems. This is how machine learning empowers data scientists to achieve the objectives of a business. 

Regression analysis

Regression analysis helps in determining the relationship between a set of variables. Data scientists use it to see if there is any correlation between a dependent and one or more independent variables. With the help of regression analysis, you can identify how independent variables are impacting the dependent variable. 

For example, let us assume that you are a data scientist trying to find out the relationship between two variables: (1) maintenance costs and (2) the number of products manufactured. Here, the number of products manufactured is an independent variable whereas maintenance cost is dependent. You can use regression analysis to determine how maintenance costs are getting affected when you manufacture more or fewer products.

Time series analysis

It refers to the analysis of data collected during a particular interval of time. Businesses use time series analysis to measure the same variable at different times and predict future trends. For example, you can use time series analysis to measure weekly sales figures and identify whether it remains stable, increase, or decreases over an extended time.

Other patterns you must look for are seasonality, i.e., how your sales fluctuate with changing seasons, and cyclic patterns, i.e., unpredictable data fluctuations. Time series analysis is used for economic predictions, sales forecasting, and stock market analysis. 

Text Mining

Over 80% of business data is in the form of text. To gain meaningful insights from this text-based data, it is important to analyse it using the right technologies. Text data mining is the process of converting unstructured text into structured one to identify meaningful patterns. Data scientists use text mining to determine customer behaviours and predict future trends.

Data from call centre transcripts, customer reviews, surveys, etc, can be used properly using text mining techniques. It allows businesses to automatically identify useful information from social media posts, emails, chatbots, surveys, customer service tickets, etc.

Predictive Analysis

Predictive analysis, used along with machine learning and other statistical tools, is a method to predict the likelihood of an event occurring in the future. Businesses use this to forecast future trends with great accuracy and make business strategies accordingly. 

It can be very effective in predicting how customers are going to respond to certain product changes. Moreover, you can identify the means to make your marketing campaigns successful and attract more customers. 

Data Visualization

Data visualization refers to the representation of data using visual elements, such as graphs, maps, charts, etc. It helps you understand and analyse the trends better. Using data visualization tools in the right manner can bring immense growth to a business. A leading global technology company reported a 95% increase in its efficiency after using data visualization.

10 Reasons to Learn Data Science

Globally, around 60% of companies use data science. The numbers are expected to register double-digit growth over the next few years. The following are some additional reasons why data scientist is a lucrative career option:
  • Data Scientists are highly in demand in every business.
  • The field of data science is less competitive compared to other jobs.
  • Data scientists are among the highest-paid professionals today.
  • Data Science is a versatile field with applications in every industry.
  • There are constant learning opportunities as the field of data science is continuously evolving.
  • The work of data scientists is considered impactful as they help their companies in making the most important decisions.
  • Data scientists get job satisfaction as they are highly valued in every organization
  • There are continuous growth opportunities. 
  • You get multiple career opportunities after learning data science. You can become a data scientist, a data analyst, a team leader, or even a manager in a company.
  • You develop knowledge and skills from a variety of other fields, such as programming, statistics, business, mathematics, etc.

How To Build a Career in Data Science?

You can build a career in data science by enrolling in a suitable data science course or degree program. If you have just completed your higher secondary education, go for a graduate degree in a related field, like mathematics, statistics, computer science, or any other quantitative subject. 

If you are already a graduate, you can look for data science courses online or offline at your convenience. It might not be essential to have a bachelor’s degree in one of the above subjects. You can have a degree in any specification, but you must be interested in learning these quantitative subjects.

Several institutes offer data science courses these days. However, not all of them provide you with comprehensive coverage. The online data science courses and data analytics certifications by Edvancer provide you with 360-degree coverage along with the necessary practical knowledge. You can check the prerequisites, syllabus, learning style, course fees, etc, and start your journey to become a data scientist.

Data Science Courses at Edvancer

Edvancer offers the following online courses in data science and data analytics:  

1. IBM Professional Certificate in Data Science: The duration of this course is 300 hours, which includes 150 hours of learning and 150 hours of projects & assignments. It covers:  

  • Business Analytics in R (linear & logistic regression, data visualization, time series forecasting, etc)
  • Machine Learning in Python (basics of data science and machine learning, text mining, neural networks, etc)
  • Data Analysis in SQL


2. Certified Data Science Specialist: This course also consists of 150 hours of learning along with 150 hours of projects. You learn machine learning in Python, Business analytics, and data analysis under this.


3. PG Program in Data Science: This course offers you a certification from one of India’s top universities, UPES. You get comprehensive coverage of data science and a UPES alumni status, which helps you secure your future as a data scientist.


4. Executive Program in Data Science for Managers: It is a short-duration program for those who are already working in the field of data science. You can develop your skills further and take your career to another level with this certification.


All the courses by Edvancer are online. However, you get two learning options with all of them (except the executive program for managers. You can choose to learn via live online classes or under the self-paced learning program. A great thing about these courses is that they allow you to develop practical knowledge by working on real projects.


1. What are some advanced techniques for data analysis?
Some advanced techniques for data analytics include Machine Learning, time series analysis, regression analysis, data visualization, text mining, predictive analysis, etc.

2. What are some challenges associated with data analysis?
Some common challenges faced by businesses in data analysis are collecting and storing such large amounts of data, using data from multiple sources, visual representation of data, and choosing the right analytics tool. However, with advanced data analysis techniques, these problems can be solved to a great extent.

3. What are some best practices for data analysis?
Some best practices for data analysis are as follows:

  • Define clear objectives
  • Clean and verify the data
  • Use consistent models
  • Visualise data to verify model accuracies

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