Data Science Training | Best Data Science Training Institute in Delhi
Overview
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Aedifico is offering the most comprehensive Data Science training in delhi available right now covering all the steps like Data Integration, Data Manipulation, Modelling Techniques etc. Master the skills to become a certified data scientist. Data science is an extremely popular field with a broad range of applications nowadays relying upon it. Data is something which is everywhere like from photos to prominent organizations financials all kind of data is getting digital, and recently all that data are being analyzed to view the performance of the agencies and help them improving the same. For better analyzing the data, every second organization from big players to SME’s are looking for professionals, freelancers who can convert there raw old data into a kind of form which makes sense and ready to be visualized and analyzed. The data scientist is a kind of profile in which is it expected to have a good sound knowledge of programming, statistics and problem-solving capabilities with the ability to find the patter out of the raw data.
Aedifico is providing best Data science training using Python. The data science knowledge is the need for the industry in current scenario. Aedifico is best known for the project-based learning. Various case studies will be put forward during the training tenure to enhance the problem-solving capabilities in the field of data analysis. Some key point of the contents are shared below and for more information contact Aedifico.
Curriculum
What is Data Science?
Python for Data Science
Data Analytics overview
Scientific computing with Python
Introduction to Numpy
Class and Attributes of ndarray
Mathematical Functions of Numpy
Scipy module
Integration and Optimization
Eigenvalues and Eigenvector
Sub Package-Statistics, Weave and IO
Understanding DataFrame
Using Pandas module
Importing data from various sources (csv, txt, excel)
Sorting, filtering, duplicates, merging, appending
Subsetting, sampling, formatting
stripping out extraneous information
Normalizing data
Matplotlib library introduction
2D graphs and 3D graphs visualisation
Introduction exploratory data analysis
Descriptive statistics,Frequency Tables
Graphical Analysis
Creating Graphs-Bar/pie/line chart/histogram/boxplot/density
Introduction to PANDAS
DATAFRAMES basics
Reading the files
Plotting the content
Preprocessing the data
Understanding of raw data
Filtering and sorting of the data
Measure of Central Tendencies and Variance
Building blocks-Probability Distribution, Normal Distribution
Inferential Statistics-Sampling, Hypothesis testing
Machine Learning Approach and Predictive Modeling
Regression vs classification vs segmentation vs forecasting
Learning algorithms-Supervised vs Unsupervised Learning
Feature engineering and dimension reduction
Concept of optimization and cost function
Scikit module of python
Segmentation and Clustering(k-means)
Artificial Neural Networks(ANN)
Support Vector Machines(SVM)
Bayesian Methods
Measuring Entropy
Decision Trees
KNN,PCA
Introduction of Text Mining using NLTK
Fine tuning the models using Hyper parameters, grid search
User-Based Collaborative Filtering
Item Based Collaborative Filtering
