About Data Science

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 in class training on Data science 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.

Course Highlights

  • Introduction to Data Science
    What is Data Science?
    Python for Data Science
    Data Analytics overview
    Scientific computing with Python
    Mathematical Computing with Python
    Introduction to Numpy
    Class and Attributes of ndarray
    Mathematical Functions of Numpy
    Scientific computing with Python
    Scipy module
    Integration and Optimization
    Eigenvalues and Eigenvector
    Sub Package-Statistics, Weave and IO
    Data Manipulation
    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
    Data Analysis and Visualization
    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
    Basic Statistic and stats methods
    Measure of Central Tendencies and Variance
    Building blocks-Probability Distribution, Normal Distribution
    Inferential Statistics-Sampling, Hypothesis testing
    Machine Learning -Basics
    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
    Machine Learning-Algorithms
    Segmentation and Clustering(k-means)
    Artificial Neural Networks(ANN)
    Support Vector Machines(SVM)
    Bayesian Methods
    Measuring Entropy
    Decision Trees
    Introduction of Text Mining using NLTK
    Fine tuning the models using Hyper parameters, grid search
    Recommender System
    User-Based Collaborative Filtering
    Item Based Collaborative Filtering

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