Data Analysis Course

Kaashiv Infotech is the best place to learn Data Analysis training where the training’s are provided with real time example.

The Data Analysis training in Chennai Syllabus is designed according to the student needs of the corporate client which will definitely help you to clear the interviews easily.

Data Analysis is a popular high-level programming language. It is mainly used as the best programming language to learn. Data Analysis is an accurate fast, easy-to-use, and easy-to-deploy programming language that is being used to develop scalable web applications.

Kaashiv Infotech is one of the best company for a Data Analysis Training . It is a great option for both beginners and experienced professionals.

Data analytics course syllabus – is given below :

1. Python Basics –  Python Programming, Python Installation
2. R- Programmming Introduction –  Business Analytics, Data, Information , Compare R with other software in analytics
3. Data Manipulation in R – Data sorting , Find and remove duplicates record , Cleaning data , Recoding data & its implementation
4. Data Processing in Data Science – Data Processing techniques & its implementation
5. Exploratory Data Analysis – Visualizing Data
6. Statistical Analysis in Data Science – Median , Quartiles , Correlation , Covariance , Regression , Linear Regression , Non Linear Regression, Multiple Regression
7. Web Scraping and Data Science  Algorithm

data analyst course fee – Applicants shall receive an email with details about payment after completing registration by filing the registration form.

The right choice by starting to learn Data Science because Data Science is the world’s fastest and most popular programming language not just among SDE’s but also among Mathematicians, Data Analysts, Scientists and even Kids too. The reason is simple because Data Science is a very user-friendly programming language. Data Science has the following applications such as data analysis, Artificial intelligence , Machine learning , Automation , Build web Apps and software testing .

Data Analysis programming is the best career option for the students. Nowadays companies are in great demand for Data Analyst. As new startups launched and increased in the work of web development, the use of Data Science increased. After learning, Data Analysis certification course near me – there are multiple job roles available.

In order to get the best training company, there are many good companies like Kaashiv infotech that focus more on the programming part of the students.

• Our trainers are more than 10+ years of experience in course relevant technologies.
• Trainers are expert level in the subjects they explain because they continue to spend time working on real-world industry applications.
• Trainers have skilled on real-time projects in their industries.
• Are working professionals working in international companies such as CTS, TCS, HCL Technologies, etc…
• Trained more than 1500+ students in a year.
• Strong theoretical & practical knowledge.

Each student will be given industry-recognized Data Analysis course completion certificate.

KaaShiv InfoTech Provides ISO Certified and Microsoft Sealed Course completion certificate for students

• Yes. We provide online training programs as well. Timing is completely selected basis the availability of the candidate. Hence very flexible and comfortable.

The Answer is, Yes . Training in Data Analysis provides lot of technological and programming knowledge to the students and enable them to become professional experts

• Students belonging to 1st year, 2nd year, 3rd year and 4th year Electronics, Instrumentation, Electronics and Telecommunications and Biomedical engineering can apply for this machine learning course.
• Students from Computer science Engineering or Information Technology Engineering can also apply for this course.

The answer is – Kaashiv infotech provides, training in Data Analysis. This Course involves, learning and developing appliclations. Various technology Courses are,

What is the process of Data Analysis ?

Data analysis is the process of collecting, cleansing, interpreting, transforming and modelling data to gather visions and generate reports to increase business profits.

• Collect Data: The data gets collected from various sources and is stored so that it can be cleaned and prepared. In this step, all the missing values and outliers are removed.
• Analyse Data: Once the data is ready, the next step is to analyse the data. A model is run frequently for improvements. Then, the mode is validated to check whether it sees the business requirements.
• Create Reports: Finally, the model is executed and then reports thus generated are approved onto the stakeholders.

What are the key differences between Data Analysis and Data Mining ?

Data analysis includes the process of cleaning, organising, and using data to produce meaningful visions. Data mining is used to search for hidden patterns in the data.

Data analysis produces results that are far more understandable by a variety of audience than the results from data mining.

What is Data Science ?

Data science is a study of the huge amount of data, which used for extracting from raw, structured, and unstructured data that is processed using the scientific method, different technologies, and algorithms.

• Uses known and labeled data as input
• Supervised learning has a feedback mechanism
• The most commonly used supervised learning algorithms are decision trees, logistic regression, and support vector machine.

Unsupervised Learning :

• Uses unlabeled data as input
• Unsupervised learning has no feedback mechanism
• The most commonly used unsupervised learning algorithms are k-means clustering, hierarchical clustering, and apriori algorithm
• Learn Data Science with R for FREE
• Learn and Master the Basics of Data Science

• Take the entire data set as input
• Calculate entropy of the target variable, as well as the predictor attributes
• Calculate your information gain of all attributes (we gain information on sorting different objects from each other)
• Choose the attribute with the highest information gain as the root node
• Repeat the same procedure on every branch until the decision node of each branch is finalized

• Randomly select ‘k’ features from a total of ‘m’ features where k << m
• Among the ‘k’ features, calculate the node D using the best split point
• Split the node into daughter nodes using the best split
• Repeat steps two and three until leaf nodes are finalized
• Build forest by repeating steps one to four for ‘n’ times to create ‘n’ number of trees.

• Overfitting refers to a model that is only set for a very small amount of data and ignores the bigger picture. There are three main methods to avoid overfitting:
• Keep the model simple—take fewer variables into account, thereby removing some of the noise in the training data
• Use cross-validation techniques, such as k folds cross-validation
• Use regularization techniques, such as LASSO, that penalize certain model parameters if they’re likely to cause overfitting

• There are two main methods for feature selection, i.e, filter, and wrapper methods.
  • Filter Methods
  • Wrapper Methods

Write a basic SQL query that lists all orders with customer information. ?

Usually, we have order tables and customer tables that contain the following columns:

The SQL query is:

SELECT OrderNumber, TotalAmount, FirstName, LastName, City, Country FROM Order JOIN Customer ON Order.CustomerId = Customer.Id