Desire Infotech provides Data Science course in Gandhinagar, the most comprehensive Data Science course in the market, covering the complete Data Science lifecycle concepts from Data Collection, Data Extraction, Data Cleansing, Data Exploration, Data Transformation, Feature Engineering, Data Integration, Data Mining, building Prediction models, Data Visualization and deploying the solution to the customer. Skills and tools ranging from Statistical Analysis, Text Mining, Regression Modelling, Hypothesis Testing, Predictive Analytics, Machine Learning, Deep Learning, Neural Networks, Natural Language Processing, Predictive Modelling, R Studio, Tableau, Spark, Hadoop, programming languages like R programming, Python are covered extensively as part of this Data Science training. Desire Infotech is considered as the best Data Science training institute in Gandhinagar which offers services from training to placement as part of the Data Science training program.
Desire Infotech is a global institute for data science in Gandhinagar rendering specialized coaching in the fields such as Data Science, Machine Learning, Deep Learning, Artificial Intelligence, the Internet of Things and Python Learning.
Desire Infotech starting its training from basic to advanced. In addition, whole training conducted by Industry Expert who has been working in the same field for more than 5 years. Last but the least, one to one focus is there.Desire Infotech is considered to be one of the best Data Science training institutes in Gandhinagar.
What is the average salary of a data scientist after data science courses?
Ans. The average salary for a data scientist in India is INR 3 LPA. Data Scientists are at the moment gaining from the big data trend with analytics professionals making 23% higher as compared to an average software engineer in India after graduating from data science courses.
Is the Data Science course still in demand?
Ans. Yes, a Data Science course has always been and is still in demand.
What is data science course eligibility?
Ans. Anyone who has completed 10+2 from a recognized board is eligible to pursue a data science course. They must’ve studied science with maths as a compulsory subject and scored more than 50% marks in each of the subjects in the class 12th exam.
STATISTICS ESSENTIALS FOR ANALYTICS
All the topics in the following section will explain the basics of what it is, which scenario you want to use, What math behind it, How to implement with an analytic tool, what inferences you are getting from the final result
Understanding the Data
Probability and its Uses
Statistical Inference
Data Clustering
Testing the Data
Regression Modelling
Data Science with Python
Module 1: Introduction to Data Science
What is Data Science?
What is Machine Learning?
What is Deep Learning?
What is AI?
Data Analytics & it’s types
Module 2: Introduction to Python
What is Python?
Why Python?
Installing Python
Python IDEs
Jupyter Notebook Overview
Module 3: Python Basics
Python Basic Data types
Lists
Slicing
IF statements
Loops
Dictionaries
Tuples
Functions
Array
Selection by position & Labels
Module 4: Python Packages
Pandas
Numpy
Sci-kit Learn
Mat-plot library
Module 5: Importing data
Reading CSV files
Saving in Python data
Loading Python data objects
Writing data to csv file
Module 6: Manipulating Data
Selecting rows/observations
Rounding Number
Selecting columns/fields
Merging data
Data aggregation
Data munging techniques
Module 7: Statistics Basics
• Central Tendency
Mean
Median
Mode
Skewness
Normal Distribution
Probability Basics
What does mean by probability?
Types of Probability
ODDS Ratio?
Standard Deviation
Data deviation & distribution
Variance
Bias variance Trade off
Underfitting
Overfitting
Distance metrics
Euclidean Distance
Manhattan Distance
Outlier analysis
What is an Outlier?
Inter Quartile Range
Box & whisker plot
Upper Whisker
Lower Whisker
Scatter plot
Cook’s Distance
Missing Value treatments
What is a NA?
Central Imputation
KNN imputation
Dummification
Correlation
Pearson correlation
Positive & Negative correlation
Module 10: Unsupervised Learning
K-Means
K-Means ++
Hierarchical Clustering
Module 11: Other Machine Learning algorithms
K – Nearest Neighbour
Naïve Bayes Classifier
Decision Tree – CART
Decision Tree – C50
Random Forest