Data Science Training

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.

Why Should You Choose Desire Infotech For Data Science Training In Gandhinagar?

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.

Syllabus:

• 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