In 2012, Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century. Companies like Google, Facebook and others collect user data and sell them to ad companies to earn profits. How do you think they know whether you like dogs or cats? How do you think Amazon knows what products to recommend to you even when they haven’t explicitly asked you about it? The answer is data. Some other major reasons why data science is popular are:

• Data-driven decision making is increasing in demand.
• Due to the lack of well-trained data scientists, professionals trained in data science are offered the highest salary in the tech world.
• Data is being collected at an exceptionally high rate, which requires an equal rate of analysis Which are the global cities in which Mentors Pool conducts Data Science with Python certification training? to make the most of it. Data scientists can help a company take crucial marketing decisions based on their findings from raw data.

There are many benefits to being in the job declared as the ‘Sexiest job of the 21st century’ by Harvard Business review:

1. High Pay: First things first, we all expect high pay from a job, especially when the qualification bar is set incredibly high. Due to high demand and low supply, data scientist jobs are one of the highest paying jobs in the IT industry today.
2. Good bonuses: Although it is a part of their pay, data scientists can expect impressive bonuses. Other perks may include equity shares and signing perks.
3. Education: By the time you become a data scientist, you would probably be having either a Masters or a PhD due to the demand for knowledge in this field. You could receive offers to work as a lecturer or as a researcher for governmental as well as private institutions.
4. Mobility: Many businesses that collect data are mostly located in developed countries. Getting a job in one would fetch you a hefty salary as well as raise your standard of living.
5. Network: Your involvement in the tech world through research papers in international journals, tech talks at conferences and many more platforms would help expand your network of data scientists. This, in turn, can be used for referral purposes as well.

Most data scientists have a PhD or master’s degree, which clearly indicates how competitive this field is. Having a certification in data science can have a great impact on your overall profile. We have compiled a list of some of the best and popular certifications for you:

• Data Science with Python from Mentors Pool
• Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
• Cloudera Certified Associate – Data Analyst
• Cloudera Certified Professional: CCP Data Engineer
• Microsoft Certified Solutions Expert
• Dell EMC Proven Professional

Data science is a huge field and covering everything about data science is not possible. So it is highly advised to decide what is your area of interest in this field. There are two ways to decide what kind of data science course you want to pursue:

• Enroll yourself in data science courses to see which topics interest you and which topics are extremely difficult to understand.
• Implement your data science skills. Through thorough implementation, you can find which step of the data science phase of the project interests you more.

We have compiled our learning path in logical sequence to help you delve into it successfully.

1. Getting started
2. Mathematics
3. Libraries
4. Data visualization
5. Data preprocessing
6. Machine Learning and Deep Learning
7. Natural Language processing
8. Polishing skills

1. Getting started: Choose a programming language in which you are comfortable. We suggest Python or R language. Understand what data science actually means and the roles and responsibilities of a data scientist.
2. Mathematics: Data science is all about making sense of raw data, finding patterns and relationship between them and finally representing them, which is why it is crucial that you have a good command over both mathematics as well as statistics. Therefore, we have compiled some of the topics which you can pay special attention to:
   1. Descriptive statistics
   2. Probability
   3. Linear algebra
   4. Inferential statistics
3. Libraries: Data science process involves various tasks ranging from preprocessing the data given to plotting the structured data and finally to applying ML algorithms as well. Some of the famous libraries are:
   1. Scikit-learn
   2. SciPy
   3. NumPy
   4. Pandas
   5. ggplot2
   6. Matplotlib
4. Data visualization: It’s your job to make sense of the data given to you by finding patterns and making it as simple as possible. The most popular way to visualize data is by creating a graph. There are various libraries that can be used for this task:
   1. Matplotlib – Python
   2. Ggplot2 – R
5. Data preprocessing: Due to the unstructured form of data, it becomes necessary for data scientists to preprocess this data in order to make it analysis-ready. Preprocessing is done using feature engineering and variable selection. After preprocessing, our data would be in a structured form and ready to be injected into ML tool for analysis.
6. ML and Deep learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist. For data analysis, deep learning is highly preferred as deep learning algorithms are designed to work when you have to deal with a huge set of data. It is recommended that you spend a few weeks on topics like neural networks, CNN, and RNN as well.
7. Natural Language processing: Every data scientist should be an expert in NLP as it involves processing of text form of data and its classification as well.
8. Polishing skills: Competitions like Kaggle etc. provide some of the best platforms to exhibit your data science skills. Apart from online competitions, you can keep on experimenting and exploring the field by creating your own projects as well.

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

• Build Python programs: distribution, user-defined functions, importing datasets and more
• Manipulate and analyse data using Pandas library
• Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
• Distribution of data: variance, standard deviation, interquartile range
• Calculating conditional probability via Hypothesis Testing
• Analysis of Variance (ANOVA)
• Building linear regression models, evaluating model parameters, and measuring performance metrics
• Using Dimensionality Reduction Technique
• Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
• Building KNN algorithm models to find the optimum value of K
• Building Decision Tree models for both regression and classification problems
• Build Python programs: distribution, user-defined functions, importing datasets and more
• Manipulate and analyse data using Pandas library
• Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
• Build data distribution models: variance, standard deviation, interquartile range
• Calculate conditional probability via Hypothesis Testing
• Perform analysis of variance (ANOVA)
• Build linear regression models, evaluate model parameters, and measure performance metrics
• Use Dimensionality Reduction
• Build Logistic Regression models, evaluate model parameters, and measure performance metrics
• Perform K-means Clustering and Hierarchical Clustering
• Build KNN algorithm models to find the optimum value of K
• Build Decision Tree models for both regression and classification problems
• Build data visualization models for Time Series data and components
• Perform exponential smoothing