Data Analytics Training in Hyderabad

This Data Analytics training in Hyderabad and Ganatech solution, you will gain practical experience by working on industry-based projects that will help you understand how to solve real-time business problems. Also, in “Data Analyst courses in Hyderabad”, we will conduct many mock interview sessions, assist in creating and upgrading your resume, and more to give you confidence and prepare you for your job interviews.

Introduction to Data Analytics:-

This course presents a gentle introduction into the concepts of data analysis, the role of a Data Analyst, and the tools that are used to perform daily functions. You will gain an understanding of the data ecosystem and the fundamentals of data analysis, such as data gathering or data mining. You will then learn the soft skills that are required to effectively communicate your data to stakeholders, and how mastering these skills can give you the option to become a data driven decision maker.

This course will help you to differentiate between the roles of a Data Analyst, and Data Engineer. You will learn the responsibilities of a Data Analyst and exactly what data analysis entails. You will be able to summarize the data ecosystem, such as databases and data warehouses. You will then uncover the major vendors within the data ecosystem and explore the various tools on-premise and in the cloud. Continue this exciting journey and discover Big Data platforms such as Hadoop, Hive, and Spark. By the end of this course you will be able to visualize the daily life of a Data Analyst, understand the different career paths that are available for data analytics, and identify the many resources available for mastering this profession.

We Provide  you real-time business Intelligence Data Analytics  and placement-focused  Data Analytics training Our Data Analytics  course includes basic to advanced levels and our course is designed to get placement in MNC companies in Hyderabad and all over India.

“The Data Analyst training in Hyderabad” in collaboration with IBM will give you expertise in Data Analysis. You can enroll in this Data Analyst course in Hyderabad led by experts in the industry, and learn the top analytics tools and techniques, the languages of R and Python, and how to work with SQL databases, create data visualizations, and apply predictive analytics and statistics in a business environment.

1. Understand the problem

2. Data Collection

3. Data Cleaning

4. Data Exploration and Analysis

5.  Interpret the results

*Understand the problem:Understanding the business problems, defining the organizational goals, and planning a lucrative solution is the first step in the analytics process. E-commerce companies often encounter issues such as predicting the return of items, giving relevant product recommendations, cancellation of orders, identifying frauds, optimizing vehicle routing, etc.

*Data Collection : Next, you need to collect transactional business data and customer-related information from the past few years to address the problems your business is facing. The data can have information about the total units that were sold for a product, the sales, and profit that were made, and also when was the order placed. Past data plays a crucial role in shaping the future of a business.

*Data Cleaning: Now, all the data you collect will often be disorderly, messy, and contain unwanted missing values. Such data is not suitable or relevant for performing data analysis. Hence, you need to clean the data to remove unwanted, redundant, and missing values to make it ready for analysis.

*Data Exploration and Analysis: After you gather the right data, the next vital step is to execute exploratory data analysis. You can use data visualization and business intelligence tools, data mining techniques, and predictive modeling to analyze, visualize, and predict future outcomes from this data. Applying these methods can tell you the impact and relationship of a certain feature as compared to other variables.

Below are the results you can get from the analysis:

• You can identify when a customer purchases the next product.
• You can understand how long it took to deliver the product.
• You get a better insight into the kind of items a customer looks for, product returns, etc.
• You will be able to predict the sales and profit for the next quarter.
• You can minimize order cancellation by dispatching only relevant products.
• You’ll be able to figure out the shortest route to deliver the product, etc.

 *Interpret the results : The final step is to interpret the results and validate if the outcomes meet your expectations. You can find out hidden patterns and future trends. This will help you gain insights that will support you with appropriate data-driven decision making.

Types of Data Analytics

There are four types of data analytics:

1. Predictive analytics
2. Descriptive analytics
3. Prescriptive analytics
4. Diagnostic analytics

Data Analytics Process Steps

There are primarily five steps involved in the data analytics process, which include:

*Data Collection: The first step in data analytics is to collect or gather relevant data from multiple sources. Data can come from different databases, web servers, log files, social media, excel and CSV files, etc.
*Data Preparation: The next step in the process is to prepare the data. It involves cleaning the data to remove unwanted and redundant values, converting it into the right format, and making it ready for analysis. It also requires data wrangling.
*Data Exploration: After the data is ready, data exploration is done using various data visualization techniques to find unseen trends from the data.
*Data Modeling: The next step is to build your predictive models using machine learning algorithms to make future predictions.
*Result interpretation: The final step in any data analytics process is to derive meaningful results and check if the output is in line with your expected results.

Data Analytics Tools

Now that we looked at the different steps involved in data analytics, let’s see the tools involved in data analytics, to perform the above steps. In this blog, we will discuss 9 data analytics tools, including a couple of programming languages that can help you perform analytics better.

1. Python
2. R
3. Tableau
4. Power BI
5. Excel
6. SQL Programming Language
7. QlikView
8. Apache Spark
9. SAS
   *Python: Python is an object-oriented open-source programming language. It supports a range of libraries for data manipulation, data visualization, and data modeling
   * R: R is an open-source programming language majorly used for numerical and statistical analysis. It provides a range of libraries for data analysis and visualization.
   * Tableau: It is a simplified data visualization and analytics tool. This helps you create a variety of visualizations to present the data interactively, build reports, and dashboards to showcase insights and trends.
   * Power BI:  Power BI is a business intelligence tool that has an easy ‘drag and drop functionality. It supports multiple data sources with features that visually appeal to data. Power BI supports features that help you ask questions to your data and get immediate insights.
   *Excel :  Microsoft Excel can be used to filter, organize, and visualize quantitative data, making it the perfect tool for performing simple data analysis. You can use a wide range of formulas, filters, and create pivot tables, charts and graphs, to synthesize the data you’ve gathered. But there’s a limit to the amount of data that Excel can handle, so you may need more powerful tools if you’d like to analyze data at scale.
   Explore common functions and formulas for data analysis in Excel.
   *SQL: Programming Language: Structured query language (SQL) is the standard language created to communicate with databases and is particularly useful when handling structured data. Used to search, add, update, and delete data, among others, SQL makes it easy to organize structured data.
   Most structured data that exists, is stored in SQL, so programs written for the language can easily unlock data for powerful results.
   * QlikView : QlikView offers interactive analytics with in-memory storage technology to analyze vast volumes of data and use data discoveries to support decision making. It provides social data discovery and interactive guided analytics. It can manipulate colossal data sets instantly with accuracy.
   * Apache Spark:   Apache Spark is an open-source data analytics engine that processes data in real-time and carries out    sophisticated analytics using SQL queries and machine learning algorithms.
   * SAS:  SAS is a statistical analysis software that can help you perform analytics,visualize data,write SQL queries,perform statistical analysis,and build machine learning models to make future predictions.
   Now that you have seen the data analytics tools, let’s jump ahead and see the applications of data analytics.

Top 10 Data Analytics Applications:

Data analytics has transformed the way industries operate, allowing for more informed decision making, optimizing operations, and delivering superior customer experiences. Across various sectors, the applications of data analytics stand out as a key driver for innovation and growth. Here’s a closer look:

1. Energy

2. Finance & Banking

3. Government & Public Sector

4. Health Care

5. Manufacturing

6. Marketing & Advertising

7. Real Estate

8. Retail & E-Commerce

9. Insurance

10. Transport & Logistics

* Energy: The energy sector’s applications of data analytics focus on consumption analysis and grid optimization. In an era of rising energy demands, efficient distribution and consumption become paramount.

Through these analytics applications, energy distribution can be optimized, and consumption patterns predicted.

* Finance & Banking: In finance and banking, the applications of data analytics are primarily directed towards fraud detection and risk management. Every transaction provides data that, when analyzed, can reveal anomalies.

This usage of data analytics reduces fraudulent activities and helps manage risks linked to loans and investments.

* Government & Public Sector: Governments utilize the applications of data analytics in policy formation and resource distribution. The vast administrative data provides insights into public needs and requirements.

These analytics applications allow for policies that are more aligned with public needs, ensuring resources are allocated wisely and public services improve.

*Health Care: In the health care domain, data analytics applications play a pivotal role in diagnosis and treatment optimization. Massive volumes of patient data are now analyzed to detect patterns and correlations.

These analytics applications guide health care professionals in making decisions that lead to enhanced patient outcomes and substantial reductions in medical expenses.

*Manufacturing : Manufacturing industries utilize data analytics applications for quality control and process efficiency. With complex machinery and operations, every stage provides vital data.

Predictive analytics helps in pre-empting manufacturing defects and refining production workflows, leading to reduced waste and superior products.

* Marketing & Advertising:  Marketing professionals harness analytics applications for precise customer segmentation and to gauge the effectiveness of their campaigns.

With the insights from these data analytics applications, businesses can target audiences more effectively and assess their campaign ROI.

* Real Estate : The real estate sector’s applications of data analytics involve property valuation and tracking market trends. The fluctuating property market generates vast amounts of data.

Real estate professionals, armed with these insights, can more accurately price properties and anticipate market movements.

*Retail & E-Commerce : The retail and e-commerce sector taps into analytics applications to gain customer insights and manage inventory. The digital footprints of online shopping are treasure troves of data.

With data analytics applications, retailers can discern customer preferences, hone pricing strategies, and oversee optimal stock levels, translating to boosted sales and cost savings.

*Insurance : In the insurance sector, data analytics applications are crucial for risk assessment and claim processing. With countless policyholders and claims, insurers rely on analytics to make accurate predictions and decisions.

Benefit: These analytics applications allow insurers to set premiums more accurately based on risk, as well as expedite claim processes, which enhances customer satisfaction and operational efficiency.

* Transport & Logistics : In transport and logistics, the applications of data analytics involve route optimization and demand prediction. The constant movement of goods provides a continuous stream of data to be processed.

With this, optimal transport routes can be deduced, and shipment demands anticipated, translating to savings and improved services.

Data analytics applications are reshaping industries, bringing forth unprecedented growth, efficiency, and customer satisfaction. As technology advances, it’s evident that their role will only become more pronounced.

Data Analytics With Python: Use Case Demo

Data is getting generated at a massive rate, by the minute. Organizations, on the other hand, are trying to explore every opportunity to make sense of this data. This is where Data analytics has become crucial in running a business successfully. It is commonly used in companies to drive profit and business growth. In this article, we’ll learn Data analytics using Python.

Why Data Analytics Using Python?

There are many programming languages available, but Python is popularly used by statisticians, engineers, and scientists to perform data analytics.

Here are some of the reasons why Data Analytics using Python has become popular:

1. Python is easy to learn and understand and has a simple syntax.
2. The programming language is scalable and flexible.
3. It has a vast collection of libraries for numerical computation and data manipulation.
4. Python provides libraries for graphics and data visualization to build plots.
5. It has broad community support to help solve many kinds of queries.

Python Libraries for Data Analytics

One of the main reasons why Data Analytics using Python has become the most preferred and popular mode of data analysis is that it provides a range of libraries.

1. NumPy
2. Pandas
3. Matplotlib
4. SciPy
5. Scikit-Learn

NumPy: NumPy supports n-dimensional arrays and provides numerical computing tools. It is useful for Linear algebra and Fourier transform.

Pandas: Pandas provides functions to handle missing data, perform mathematical operations, and manipulate the data.

Matplotlib: Matplotlib library is commonly used for plotting data points and creating interactive visualizations of the data.

SciPy: SciPy library is used for scientific computing. It contains modules for optimization, linear algebra, integration, interpolation, special functions, signal and image processing.

Scikit-Learn: Scikit-Learn library has features that allow you to build regression, classification, and clustering models.

Now, let’s look at how to perform data analytics using Python and its libraries.

Life Cycle Phases of Data Analytics

In this article, we are going to discuss life cycle phases of data analytics in which we will cover various life cycle phases and will discuss them one by one.

Data Analytics Life cycle :
The Data analytic lifecycle is designed for Big Data problems and data science projects. The cycle is iterative to represent real project. To address the distinct requirements for performing analysis on Big Data, step – by – step methodology is needed to organize the activities and tasks involved with acquiring, processing, analyzing, and re purposing data.

Phase 1: Discovery 

• The data science team learn and investigate the problem.
• Develop context and understanding.
• Come to know about data sources needed and available for the project.
• The team formulates initial hypothesis that can be later tested with data.

Phase 2: Data Preparation 

• Steps to explore, pre process, and condition data prior to modeling and analysis.
• It requires the presence of an analytic sandbox, the team execute, load, and transform, to get data into the sandbox.
• Data preparation tasks are likely to be performed multiple times and not in predefined order.
• Several tools commonly used for this phase are – Hadoop, Alpine Miner, Open Refine, etc.

Phase 3: Model Planning 

• Team explores data to learn about relationships between variables and subsequently, selects key variables and the most suitable models.
• In this phase, data science team develop data sets for training, testing, and production purposes.
• Team builds and executes models based on the work done in the model planning phase.
• Several tools commonly used for this phase are – Matlab, STATISTICAL.

Phase 4: Model Building 

• Team develops datasets for testing, training, and production purposes.
• Team also considers whether its existing tools will suffice for running the models or if they need more robust environment for executing models.
• Free or open-source tools – Rand PL/R, Octave, WEAK.
• Commercial tools – Matlab , STATISTICAL.

Phase 5: Communication Results

• After executing model team need to compare outcomes of modeling to criteria established for success and failure.
• Team considers how best to articulate findings and outcomes to various team members and stakeholders, taking into account warning, assumptions.
• Team should identify key findings, quantify business value, and develop narrative to summarize and convey findings to stakeholders.

Phase 6: Ope rationalize 

• The team communicates benefits of project more broadly and sets up pilot project to deploy work in controlled way before broadening the work to full enterprise of users.
• This approach enables team to learn about performance and related constraints of the model in production environment on small scale &nbsp, and make adjustments before full deployment.
• The team delivers final reports, briefings, codes.
• Free or open source tools – Octave, WEAK, SQL, Mad lib.

Objectives Of The Course:

• Complete understanding of the Big Data Analytics Concepts.
• Understand the different Data Processing skills.
• Real-Time Analysis on Large Data.

Pre Requisites:-

• System analyst and Data Analysts.
• Business Intelligence and Business Professionals.
• No Need of Programming Experience.
• Any one wants to learn Big Data Analytics.

Big Data Analytics Course Highlights:-

• Weekly  Tasks
• Exam after Each Session
• Mini Project for each Data Mining Topic
• Power point Presentation, R, SAS Program with Data on each Day
• Real-time Project on Data Analytics

What are the skills required to take up this Data Analyst course in Hyderabad?

To be a successful data analyst, you need strong math and analytical skills. You must be able to think logically and solve problems, and have attention to detail. Additionally, you must be able to effectively communicate your findings to those who will make decisions based on your analysis.

 Big Data Analytics Training Overview:-

This Big Data Analytics training includes managing, storing and processing of Big Data, as well as analytics layer. For analyzing and processing the Data at maximum speed. Big Data Analytics course will give the necessary skills to deploy different analytical tools and techniques to work on Big Data and Hadoop.

Why should you build your career in Data Analytics in Hyderabad?

The data analyst role helps an organization make better decisions, be more competitive, or even shape the workforce culture. Because the information you provide is critical to a business’s bottom line, you have the opportunity to be a data-driven leader.

What skills are needed to be a Data Analytics?

A data scientist Analytics should be an expert in multiple skills such as Mathematics, statistics, Big Data.
Data Analysis should have a deeper understanding of Mathematics, Statistics and should have good analytical skills to understand patterns in these data.
We should be able to develop machine learning algorithms that are capable of doing predictive analysis and generate sample data based on given datasets.

Who can enroll in this Data Analytics Certification Training in Hyderabad?

Following are the individuals who must take up this Data Analytics course in Hyderabad and up skill themselves:

• Marketing Managers
• Banking Professionals
• Sales Professionals
• IT Professionals
• Data Analytics Professionals
• Supply Chain Network Managers

      Different Modes of  Best Data Analytics Training in Hyderabad:-

Get enrolled for the most demanding skill in the world. “Best Data Analytics Training in Hyderabad”. will make your career a new height. We at Gana tech solution provide you with an excellent platform to learn and explore the subject from industry experts. We help students to dream high and achieve it. We have Data experts as our trainers. Our trainers have been working in top MNCs in good Data Analytics roles and have good experience in training students. We provide training in both classroom and online modes. We do Corporate training.

If you are a business, you sure have trust in your employees. They are efficient, no doubt. But by offering them proper data Analytics training, we will make them capable of having better insights into your company’s data. Our curriculum is comprehensive and is suitable for all kinds of employees-both from IT and non-IT backgrounds. Interestingly, our training is customization. And we facilitate customization because we acknowledge that your company is unique and its needs are also special. With customized training to your employees, we help them improve their data Analytics capabilities. And guess what! The training is imparted by excellent instructors, having industry experience. Don’t worry about time and place compatibility, we also offer online data Analytics training.

Data Analytics Training in Ameerpet:-

Best Data Analytics Training in Ameerpet. To work as a data analyst you must have a working understanding of R, Python, SAS, SPSS, or Hadoop, among other abilities. Knowledge of data visualisation technologies such as
SQL, Python, Python Analytics, Excel Analytics, Tableau and Power BI with projects adds to the versatility of
the profile. Data analytics includes studying diverse data sets and forecasting and marketing plans utilising
predictive and dimensional data modelling.