What do you do with Data science ?
So, before being prepared for handling, all information experiences pre-handling. This is a fundamental gathering of tasks that convert crude information into an arrangement that is progressively reasonable and consequently, helpful for additional handling. Regular procedures are:
Gather crude information and store it on a server
This is immaculate information that researchers can’t examine straight away. This information can emerge out of studies, or through the more well known programmed information assortment worldview, similar to treats on a site.
Class-name the perceptions
This comprises of orchestrating information by class or marking information that focuses on the right information type. For instance, numerical, or straight out.
Information purifying/information scouring
Managing conflicting information, as incorrectly spelled classes and missing qualities.
On the off chance that the information is lopsided with the end goal that the Data Science Online Training classes contain an inconsistent number of perceptions and are in this manner not delegate, applying information adjusting techniques, such as removing an equivalent number of perceptions for every classification, and setting up that for handling, fixes the issue.
Re-masterminding information focuses to wipe out undesirable examples and improve prescient execution further on. This is applied when, for instance, if the initial 100 perceptions in the information are from the initial 100 individuals who have utilized a site; the information isn’t randomized, and designs because of examining rise.
What is Data science certification?
It’s unmistakable that Data Science has an exceptionally encouraging future and has a great deal of extension. There is an enormous deficiency of HR right now, particularly in India; it is evaluated that by 2019, there will be a setback of 1.5 million information researchers. Remembering this, the two understudies and experts are for the most part ready to have an edge over every other candidate on the off chance that they influence their degree or certification on the equivalent. A portion of the courses worth referencing is:
Coursera-Data Science Specialization: This Specialization covers the ideas and devices you’ll require all through the whole information science pipeline, from posing the correct sorts of inquiries to making derivations and distributing results. In the last Capstone Project, you’ll apply the abilities learned by building an information item utilizing certifiable information. At the finish, understudies will have a portfolio showing their dominance of the material.
Microsoft-Professional Program for Data Science: Microsoft counseled information researchers and the organizations that utilize them to distinguish the center aptitudes they should be effective. This educated the educational plan used to show key utilitarian and specialized aptitudes, joining exceptionally evaluated Data Science online training courses with hands-on labs, deducing in the last capstone venture.
edX-Data Science Course: Multiple course programs exist to get you on a way to a vocation as an information researcher. The Micro Master’s program shows you fundamental Python programming expected to perform information assignments and investigates AI and large information examination utilizing Spark. Also, finishing a Micro Masters can kick off an information science certificate or information science aces. The Programs highlight multi-course tracks intended to give you top to bottom information and preparing. Data Science Online training and online classes are available in this certification.
What are the prerequisites for the certification?
What is Data Science?
Why Python for data science?
Relevance in industry and need of the hour
How leading companies are harnessing the power of Data Science with Python?
Different phases of a typical Analytics/Data Science projects and role of python
Anaconda vs. Python
Overview of Python- Starting with Python
Introduction to installation of Python
Introduction to Python Editors
Understand Jupyter notebook & Customize Settings
Concept of Packages/Libraries – Important packages(NumPy, SciPy, scikit-learn, Pandas, Matplotlib, etc)
Installing & loading Packages & Name Spaces
Data Types & Data objects/structures (strings, Tuples, Lists, Dictionaries)
List and Dictionary Comprehensions
Variable & Value Labels – Date & Time Values
Basic Operations – Mathematical – string – date
Reading and writing data
Numpy, scify, pandas, scikitlearn etc
Importing Data from various sources (CSV, txt, excel, access etc)
Database Input (Connecting to the database)
Viewing Data objects
Exporting Data to various formats
Important python modules: Pandas, selenium, pandas SQL, python integration with HTML
Cleansing Data with Python
Data Manipulation steps(Sorting, filtering, duplicates, merging, derived variables, sampling, Data type conversions, renaming, formatting etc)
Data manipulation tools(Operators, Functions, Packages, control structures, Loops, arrays etc)
Python Built-in Functions (Text, numeric, date, utility functions)
Python User Defined Functions
Important Python modules for data manipulation (Pandas, Numpy, re, math, string, DateTime etc)
Introduction exploratory data analysis
Descriptive statistics, Frequency Tables and summarization
Univariate Analysis (Distribution of data & Graphical Analysis)
Bivariate Analysis(Cross Tabs, Distributions & Relationships, Graphical Analysis)
Important Packages for Exploratory Analysis(NumPy Arrays, Matplotlib, Plotly, seaborn, bokeh, Pandas and scipy.stats etc)
Basic Statistics – Measures of Central Tendencies and Variance
Building blocks – Probability Distributions – Normal distribution – Central Limit Theorem
Inferential Statistics -Sampling – Concept of Hypothesis Testing
Statistical Methods – Z/t-tests (One sample, independent, paired), Anova, Correlation and Chi-square
Important modules for statistical methods: Numpy, Scipy, Pandas
Introduction to Machine Learning & Predictive Modeling
Types of Business problems – Mapping of Techniques – Regression vs. classification vs. segmentation vs. Forecasting
Major Classes of Learning Algorithms -Supervised vs Unsupervised Learning
Different Phases of Predictive Modeling (Data Pre-processing, Sampling, Model Building, Validation)
Overfitting (Bias-Variance Trade off) & Performance Metrics
Concept of gradient descent algorithm
Concept of Cross validation(Bootstrapping, K-Fold validation etc)
Model performance metrics (R-square, RMSE, MAPE, AUC, ROC curve, recall, precision, sensitivity, specificity, confusion metrics )
Linear Regression Single Variable
Linear Regression Multiple Variables
Gradient descent and Cost Function
Save Model using joblib and pickle
Dummy variable and one hot encoding
Training and testing Data
Logistic Regression (Binary Classification)
Logistic Regression (Multiclass Classification)
Support Vector Machine (SVM)
K Fold Cross Validation
K Means Clustering
Deep Learning: Tensorflow And Keras: Introduction and Installation
Tensorflow & Keras – Neural Network For Image Classification
(Tensorflow2.0, Keras & Python) – Movie Review Classification
Introduction to MySQL
Doing Advanced Queries
Getting Started with Tableau
Data Roles: Dimension vs. Measure
Data Roles: Continuous vs. Discrete
Joins in Tableau
Prepare your Data for Analysis
Sorting of Data
What is a Group
Grand totals and Subtotals
Formatting and Annotations
Different Charts Tableau
Create Calculated fields and Parameters
H lookup and V lookup