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Let's start with machine learningIn short, machine learning algorithms are algorithms that learn (often predictive) models from data. I.e., instead of formulating "rules" manually, a machine learning algorithm will learn the model for you.So, let me give you an example to illustrate what that means! Say you are interested in implementing a spam filter. The probably most conservative approach would be to let a person sort these emails manually. Now, the "traditional" programming approach would be to look at some example emails (and/or use your "domain knowledge") to come up with a chain of rules like"if this email contains word X, label it as spam, else if email contains ..."Now, machine learning algorithms help you formulating these rules. Or in other words, (supervised) machine learning algorithms will look at a dataset of labeled emails (spam and non-spam) and derive rules from there to separate the two classes.So, what is Data Science then?First of all, data science is a pretty ambiguous, ill-defined term and interdisciplinary field; and people mean (expect) different things in different contexts. In my opinion, in practice, data science is pretty much the same as what we've known as data mining or KDD (Knowledge Discovery in Databases). The typical skills of a data scientists areComputer science: programming, hardware understanding, etc.Math: Linear algebra, calculus, statisticsCommunication: visualization and presentationDomain knowledgeWhere machine learning -- at its core -- is about the use and development of these learning algorithms, data science is more about the extraction of knowledge from data to answer particular question or solve particular problems.Machine learning is often a big part of a "data science" project, e.g., it is often heavily used for exploratory analysis and discovery (clustering algorithms) and building predictive models (supervised learning algorithms). However, in data science, you often also worry about the collection, wrangling, and cleaning of your data (i.e., data engineering), and eventually, you want to draw conclusions from your data that help you solve a particular problem.There are numerous examples of data science applications. Assume you are working for a credit company. Your boss gives you the task to find out whether a customer is creditworthy or not. You collect transaction data, maybe shipping records and customer ratings and so forth. Next, you'll probably use a machine learning algorithm to learn a predictive model. For example, let's assume you chose to grow a decision tree, and you concluded that this particular customer is not creditworthy. Finally, you prepare a nice presentation visualizing the decision tree to answer your boss' next question: Why is this customer not creditworthy? ...

To get to this answer, let me spend a little time explaining what it takes to be a data scientist. Then we’ll judge how hard it is.Going by what the market calls a data scientist, I see a few variants:people who primarily do data-based reporting work. Without being harsh on such people, this is easy if you are good with Excel, know how to make reasonably informative charts and graphs, and have a basic idea of sense-checking your conclusions.people who dig for insights in data - sometimes called data-mining work. To get good at this, you need (1), plus a fair control over statistics. This means you should know basic statistics (descriptive stats, probability, distributions) and hypothesis testing at the least. This takes time and practice to develop. Consider some good books / coursework. If you keep at it, you can start seeing results in ~6–8 months. Sometimes, in a challenging version of this work you are required to query REALLY large datasets / databases. Here knowledge of database systems, and perhaps distributed computing helps. Count additional 8–9 months if that is included. Data visualization itself is an art which perfects with practice and experience, so its hard to put a time stamp on it.people who build predictive models - sometimes called machine learning work. To get good at this you need (1)+(2)+machine learning knowledge. This means you need to know things starting from basics of what predictive models are to regression and classification models (if not reinforcement learning too). Additionally, this needs to be backed up by knowledge of linear algebra - vectors, matrices - and at least basic calculus - functions and differentiation. Most people I have seen within this sphere come in 2 layers:People who mainly use standard algorithms. Mostly, 95% of their work revolves around using linear / polynomial / non-linear regression, logistic regression, SVM, decision trees, random forests and GBMs. Most of these algorithms come pre-packaged in a statistical language like R or python. The theoretical development (reading good number of books, following several courses) is around 1.5–2 years if you are consistent. Add the time you might need to learn at least one of these languages, and you are looking at something like 2–3 years.People who use advanced machine learning algorithms, or develop their own algorithms. At this stage you are chasing after neural nets, deep learning, reinforcement learning, collaborative filtering and recommender systems. Factor in much better knowledge of linear algebra (including theories and heuristics of sparse matrices), more calculus and optimization (to be able to formulate your own cost equations, and build programs to optimize it to convergence). This is REALLY where the herd starts thinning out. As you can guess, to develop a skilled hand at this level, you need anywhere around 4–5 years of persistent effort, serious studying, and loads of practice. Combining Kaggle with some serious coursework, books and plenty of time is the recipe to get here. Additionally, to develop and deploy your own algorithms you might also have to push yourself to be proficient with “proper” software engineering - involving learning C++ / Java. More time.lastly, people who develop their own machine learning systems to solve domain specific or cross-domain problems. Sometimes called unicorns, this is asking yourself to be equivalent of a 25 member team. You want (1)+(2)+(3). Plus domain (industry) knowledge. Plus seriously good software engineering skills. Plus understanding of databases, distributed computing, computer networks … . If you get here, let me know. I will want your autograph.

In simple words, Data mining is the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses.It has a huge scope is small as well as big organizations. Data mining is basically is used in opposite direction to that of Data warehousing. Like By analyzing customers data of a company, data mining tools can build a predictive model that can tell you which customers are at risk or a loss.The most commonly used techniques in data mining are:Artificial neural networks: Non-linear predictive models that learn through training and resemble biological neural networks in structure.Decision trees: Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Specific decision tree methods include Classification and Regression Trees (CART) and Chi Square Automatic Interaction Detection (CHAID).Genetic algorithms: Optimization techniques that use process such as genetic combination, mutation, and natural selection in a design based on the concepts of evolution.Nearest neighbour method: A technique that classifies each record in a dataset based on a combination of the classes of the k record(s) most similar to it in a historical dataset (where k ³ 1). Sometimes called the k-nearest neighbour technique.Rule induction: The extraction of useful if-then rules from data based on statistical significance.***Keep Learning***