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Data Analytics training in Ahmedabad

Learn how to analyze data using Python. This course will take you from the basics of Python to exploring many different types of data. You will learn how to prepare data for analysis, perform simple statistical analysis, create meaningful data visualizations, predict future trends from data, and more!

Data Analysis libraries: will learn to use Pandas, Numpy and Scipy libraries to work with a sample dataset. We will introduce you to pandas, an open-source library, and we will use it to load, manipulate, analyze, and visualize cool datasets. Then we will introduce you to another open-source library, scikit-learn, and we will use some of its machine learning algorithms to build smart models and make cool predictions.

What is Data Analytics.

As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.

The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.

Generally, this process begins with descriptive analytics. This is the process of describing historical trends in data. Descriptive analytics aims to answer the question “what happened?” This often involves measuring traditional indicators such as return on investment (ROI). The indicators used will be different for each industry. Descriptive analytics does not make predictions or directly inform decisions. It focuses on summarizing data in a meaningful and descriptive way.

The next essential part of data analytics is advanced analytics. This part of data science takes advantage of advanced tools to extract data, make predictions and discover trends. These tools include classical statistics as well as machine learning. Machine learning technologies such as neural networks, natural language processing, sentiment analysis and more enable advanced analytics.

Why you need to learn Data Analytics?

The economy is starting to shift due to data. Look at Amazon’s rise to the top, and the effect the company has had on retail. It’s an impact that other fields, such as the civic sector, are now trying to replicate.

Why you should study data analysis is simple: Data analysis is the future, and the future will demand skills for jobs as functional analysts, data engineers, data scientists, and advanced analysts. According to O*NET, the projected growth for data analysts is 8% between 2019-2029. On average, data analysts earned $94,280 in 2019. However, salary compensation for data analysts varies depending on where they work and what industry they work in.

and Speed
Readable and
Suitable for
building products
  • Help to get knowledge of core Data Analysis concepts
  • Get the core knowledge of Data Interpretation
  • Learn to get useful insight from data and applying predictive algorithm on it.
  • Proven and well tested teaching methods for beginner
  • Get mentored by industry best Data Analytics expert
  • Learn to develop industry standard Data Analytics projects

If you are planning to learn Python from the professionals, get in touch with us now!

For any questions, you can contact us or visit LogicRays Academy center.

Data Analytics Course Details
  • First steps with python
  • Python variables and data-types
  • Branching and loops
  • Function and scope
  • Data visualization guide
  • Data visualization using matplotlib
  • Introduction to Data
  • Univariate Data
  • Multivariate Data
  • Populations and Samples
  • Confidence Interval
  • Hypothesis Testing
  • Case studies & applications of the inferential procedures
  • Exploring stackoverflow survey data
  • Introduction of Statistical Model
  • Fitting Models to Independent Data
  • Fitting Models to Dependent Data
  • Introduction to Supervised ML
  • Overfitting & Underfitting
  • KNN - classification & regression
  • Least-Squares linear regression
  • Ridge, Lasso & Polynomial Regression
  • Logistic Regression
  • Support Vector Machine
  • Multiclass Classification
  • Cross Validation
  • Decision Tree
  • Model Evaluation & Selection
  • Confusion Matrics
  • Classifier Decision Function
  • Precision-recall and ROC curves
  • Multi-Class Evaluation
  • Regression Evaluation
  • Model Selection: Optimizing Classifiers for Different Evaluation Metrics
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