Data Science Training in PureEco Tech Solution
Looking for best institute for Data Science Course in Pune! At PureEco Tech Solution experienced working professionals are offering the Data Science Course to the candidates and working professionals.
It will help you master skills and tools like Statistics, Hypothesis testing, Clustering, Decision trees, Linear and Logistic regression, Data Visualization, Regression models, My SQL, Statistical procedures, tools and analytics, and many more. This course also covers a capstone project which encompasses all the key aspects from data extraction, cleaning, visualization to model building and tuning. These skills will help you prepare for the role of a Data Scientist. In this course, you can explore what is Data Science, components skills required, model building process, career options, recent trends…..
Each and Every concept is taught practically with real-life examples in our Data Science Course. If you really interested to Learn Best Data Science Course in Pune, then PureEco Tech Solution is the Right place for you.
What is Data Science?
Data science is the study of where information comes from, what it represents and how it can be turned into a valuable resource in the creation of business and IT strategies. Mining large amounts of structured and unstructured data to identify patterns can help an organization rein in costs, increase efficiencies, recognize new market opportunities and increase the organization’s competitive advantage. The data science field employs mathematics, statistics and computer science disciplines, and incorporates techniques like machine learning, cluster analysis, data mining and visualization.
How do banks identify which customers are likely to be the most loyal, predicting flight delay, how does a website like Netflix recommends you the videos, , How does Facebook automatically tag the faces of individuals?
What is scope of career growth in Data Science after Training?
Companies have recognized the immense business value which can be delivered using data. Google, Amazon, Facebook, Mu Sigma, Fractal, AbsolutData, IBM, Accenture etc. are just some of the companies which have made investments in data products.
Scope: Salary & Jobs in data science in India
- The package offered with the least being 8 LPA can vary upto 12 LPA for a beginner , the package totally depends on the depth of knowledge you wield on this particular field
- If you are fresher, you can expect anywhere in range of 8-12 LPA rupees per Annum.
Why to choose Data science?
In the Next Few Years, It is expected that the size of the data scientist/analytics market will evolve to at least one-thirds of the global IT market from the current one-tenths. All the organizations whether large and small – are clamoring to find employees who can understand and synthesize data, and then communicate these findings in a way that proves beneficial to the company and help the management to make decisions
Who can learn Data Science?
Data science is not Rocket Science. Students, Professionals, teachers, and anyone who wishes to know the science behind data science. There are people from diverse backgrounds like chemical engineering, physics, economics, statistics, mathematics, operations research, computer science, etc who work as Data Scientists. We will get many data scientists with a bachelor’s degree in statistics and machine learning. However, having familiarity with the basic concepts of Programming, Math and Statistics, etc. is important to learn data science.
Here is the list of few things that an aspiring data scientist has within himself/herself:-
- Love to work with equations and find conclusions through it.
- Love keeping day to day information and analyzing it to set your goals.
- Love to create visuals out of data.
- Interested in knowing what is the technology behind all that is trending these days, say Siri, Google Assistant etc.
- Have great interest at knowing the future by using technology.
- Have interest in Artificial intelligence. Have a dream of making sci-fi a reality.
- Want to be heading the direction of investment for an organization.
If you know what a data scientist is and it came to your mind that you’d like to learn it, and then grab the opportunity of learning it from PureEco Tech Solution! Contact Us for more details +91 9922615656
What you will learn?
- Mathematics for Data Analysis
- Statistical inferences
- Python for Data Analysis
- NumPy
- Pandas
- Data Visualization
- Exploratory Data Analysis
- ML Models – Regression (supervised), Classification (supervised), and Clustering (unsupervised)
- Naive Bayes
- SVM
- Tree Models
- Boosting
- Principal Component Analysis
- Deep learning
- Neural Networks
- Computer Vision
- Natural Language Processing
Career Opportunities
- Python Developer
- Machine Learning Engineer
- Artificial Intelligence Engineer
- Data Scientist
- Software Engineer – Python
- Develops Engineer – Python
- Python Developer – Django
Data Science Syllabus
Mathematics for Data Analysis
- Linear Algebra (Basic linear Algebra, Matrix operations, vectors)
- Calculus (Basic differentiation)
Statistical inferences
- Mean, Mode, Median, standard deviation of data
- Sampling (mean and variance) to represent population
- Hypothesis testing (p value , critical value )
Python for Data Analysis
- Python Introduction
- Python Basic (Data types, Strings, Numbers, variables)
- Data Structures in Python
- List, Tuple, Data Dictionary, Set
- Control Structures
- Conditional statements (IF, Else ..)
- Loops (FOR, Do While)
- Python user defined Functions (lambda, Function)
- Python built-in Functions (map , filter, reduce)
- Python Classes
- Data Manipulation in Python
- NumPy
- Numpy Basics
- Ndarray Creation
- Subset, Slice, Index and Iterate through Arrays
- Operations on Numpy
- Basic operations(reshape,hstack,vstack)
- Basic Linear Algebra Operations (Matrix addition, subtraction etc
- User defined functions on array (vectorizer)
- Pandas
- Data Frame introduction
- Data Frame creation
- Data Acquisition (CSV,EXCEL,Databases ,JSON etc)
- Data Manipulation in data frame
- Slicing and dicing of data
- Selection and Filtering of data
- Groupby in dataframe
- Pivot table
- Data Frame Functions
- Data Frame Merging, concatenation
- Data Visualization
- Plots using Matplotlib& Seaborn
- Sub-Plots
- Univariate plots (Histograms, Density plots ,count plot)
- Bivariate plots (heat map)
- Plotting Categorical data (boxplot, barplot , tsplot )
- Exploratory Data Analysis
- Data visualization and data summarization
- Data imputation and data Cleaning
- Outlier treatment
- Missing data creation
- Data Understating (categorical data, numeric data)
- Machine learning Models
- Introduction to machine learning
- Supervised and unsupervised learning methods
- Various Machine learning model
- Regression (supervised)
- Classification (supervised)
- Clustering (unsupervised)
Regression:
Simple Linear Regression
Multiple Linear Regressions
- Linear regression introduction
- Cost function, optimization
- Data understanding preparation
- Model Building using python
- Model evaluation (RSS, TSS)
Classification:
Logistic Regression
- Logistic regression introduction
- Sigmoid Curve
- Cost function, optimization
- Data understanding and preparation
- Model Building using python
- Model evaluation (Accuracy, Sensitivity and Specificity,ROC Curve)
Naive Bayes
- Bayes’ Theorem
- Conditional Probability and Its Intuition
- Naive Bayes for Text Classification
- Data understanding and preparation
- Naive Bayes model using python
SVM
- SVM
- Concept of Margin Classifier
- Kernels
- Data understanding and preparation
- SVM using python
Tree Models
- Decision Trees
- Truncation and Pruning
- Random Forests
- Data understanding and preparation
- Tree models using python
Boosting
- Introduction of Boosting
- AdaBoost Algorithm
- Data understanding and preparation
- AdaBoost using python
Clustering:
- Introduction to Clustering
- K Means Clustering
- Hierarchical Clustering
- Data understanding and preparation
- Clustering using python
Principal Component Analysis:
- Understanding PCA
- Singular Value Decomposition
- Data understanding and preparation
- PCA using python
Relational Databases, SQL
Neural Networks
Natural Language Processing (NLP)
Deep Learning
- Finance Industry
- Banking Industry
- Retail Industry
- E-Commerce Industry